California Animal Nutrition Conference Proceedings May Not Be Copied Without the Written Permission of the Individual Authors

2017

CALIFORNIA

ANIMAL

NUTRITION

CONFERENCE

MAY 10 & 11, 2017 DOUBLETREE BY HILTON FRESNO CONVENTION CENTER FRESNO, CALIFORNIA IN CONJUNCTION WITH A TECHNICAL SYMPOSIUM SPONSORED BY BIOMIN AMERICA

The contents of the California Animal Nutrition Conference proceedings may not be copied without the written permission of the individual authors. 2017 CANC SPONSORSHIPS

PLATINUM SPONSORS: A.L. Gilbert Co. Marit Arana, Ph.D. (209) 847-1721 Alltech Caley Heiman (559) 226-0405 DHI Computing Service/EZfeed L.Muirbrook/N.Ver Steeg (801) 361-8512 EDGE Dairy Consulting Michael DeGroot, Ph.D. (559) 859-6563 Elanco Animal Health Phillip Jardon, DVM, MPVM (559) 381-7152 Foster Commodities Bob Myers (209) 678-5693 Kemin Industries Charity Faber (515) 559-5296 Milk Specialties Global J.Loften/T.Harris (320) 260-1695 Mycogen Seeds Vernal Gomes (509) 713-4037 Nutra Blend, LLC Sarah Weber (559) 416-6612 Nutrius LLC Patti Murphy (909) 214-6010 Pacific Elements Kevin Swager (209) 541-1216 Pine Creek Nutrition Service, Inc. Daugherty/Tully/Vieira/Stroup/Brixey (209) 667-4181 Western Milling, LLC David Ledgerwood (559) 730-6938

GOLD SPONSORS: ADM Gabi Acetoze (559) 438-0049 AminoPlus Dave Gast (402) 496-7809 Balchem - Animal Nutrition & Health Dave Ellis (402) 452-7311 CHR HANSEN Robert McCoy (559) 280-7299 Dairy Nutrition Plus aka SoyPlus/SoyChlor Johnny B.Ware (602) 762-6424 DairyWorks, LLC Steve Riesenbeck (209) 667-4181 Diversified Dairy Solutions, LLC Doug DeGroff (559) 280-7880 Enz-A-Bac Advanced Products Geoff Gates (208) 736-1607 Feedworks USA Ltd. Peter Williams (530) 758-6100 Global Agri-Trade Corporation Jurgen Godau (562) 320-8550 Harris Feeding Company Tucker Knutz (559) 884-2435 J.R. Simplot Agribusiness Jerry Marshall (209) 531-3256 MicroBasics Mark Kane-Berman (858) 756-9447 Micronutrients Amanda Lacey (317) 610-1069 Premier Magnesia, LLC Dan Nason (913) 671-7979 Progressive Dairy Solutions, Inc. Matt Budine (209) 847-1377 Richardson Oilseed Limited Francis Kolla (204) 934-5153 SoyBest Charlie Macgregor, Ph.D.,PAS (360) 631-2112 Westway Feed Products, LLC Dan Rigsbee (559) 260-0494 Zinpro Corporation Julian Casas, DVM, MPVM (559) 747-4390

SILVER SPONSORS: Almond Alliance of California Kelly Covello (209) 300-7140 Associated Feed & Supply Co. Chris Benevedes (209) 664-3357 Diamond V John Miller (559) 731-7146 Innovative Liquids LLC Luis Rodriguez, Ph.D. (916) 833-1280 J.D. Heiskell & Co. Amanda Flora (559) 687-6112 Oak Valley Community Bank Rick McCarty (209) 844-7503 Penny Newman Grain Mark Delano (559) 448-8800 QualiTech, Inc. Maria Durnbaugh (952) 448-5151 Rock River Laboratory West, Inc. Zachery Meyer (920) 261-0446 Virtus Nutrition Renee Smith (661) 699-8552 2017 CANC SPONSORSHIPS Continued......

BRONZE SPONSORS: BioTech Associates Ltd., Inc. Chuck Parks (559) 486-1569 Brandt Helen Crumpler (559) 499-2100 Evonik Corporation Don Sneddon (503) 887-7515 Feed Components Luis C. Solorzano, Ph.D. (608) 692-0898 Global Animal Products Inc. Jody Sullivan (806) 622-9600 James Farrell & Co. Rick Corra (806) 674-3750 Mission Ag Resources Deb Mize (559) 591-3333 Multimin USA Courtney Blasingame (970) 372-2302 NuWest Milling, LLC John Austel (209) 883-1163 Seley & Co. Jim Seley (626) 799-1196 Veterinary Service Inc. Kelley Thurman (559) 485-7474 William C. Loughlin & Co. Brad Barr (209) 471-5888

2017 CANC Student Poster Presentation Sponsors:

BioTech Associates Ltd., Inc. Chuck Parks (559) 486-1569 Boer Commodities Inc. Dennis Ralston (559) 253-7055 Custom Dairy Performance Inc. M.J. Bakke, P.A.S. (559) 348-3818 Nutri-Systems, Inc. T.Riordan/A.Riordan (559) 298-9134 TABLE OF CONTENTS

TECHNICAL SYMPOSIUM Sponsored by BIOMIN America

Title & Author Page

Potential for Feeding Program to Impact Gut Health and Inflammation in Dairy Cattle...... 1 Tanya Gressley, Ph.D. University of Delaware

Effects of Stress and Endotoxins on Intestinal Integrity and Function in Pigs...... 50 Nicholas Gabler, Ph.D. Iowa State University

Mycotoxin Effects: Making Sense of Complex Biological Interactions...... 98 Duarte Diaz, Ph.D. University of Arizona

Occurrence of Mycotoxins in California Dairy Feed...... 126 Paige Gott, Ph.D. BIOMIN America CALIFORNIA ANIMAL NUTRITION CONFERENCE

Title & Author Page

California Animal Nutrition Scholarship Winner Presentation...... 159

Livestock's Contributions to Climate Change: Facts and Fiction...... 160 Keynote Speaker: Frank Mitloehner, Ph.D. University of California, Davis

The California ARPAS Alfalfa Hay Project - California Chapter of ARPAS...... 164 Carl Old, Ph.D. Ruminant Nutritionist

An Update on In Vitro Evaluation Systems for NDF, Protein, and Starch...... No Paper Ralph Ward Cumberland Valley Analytical Services

Applications of uNDF in Ration Formulation and Feedbunk Management...... 176 Rick Grant, Ph.D., President William H. Miner Agricultural Research Institute

Carbohydrates: Measuring Them and Managing Them in Dairy Cattle Rations..... 218 Mary Beth Hall, Ph.D. USDA-Agricultural Research Center, Madison, WI

Biology, Behavior, and Management of Nuisance Flies Associated with Animal Agriculture...... 228 Alec C. Gerry, Ph.D. UC Extension Specialist, University of California, Riverside

Creating the Perfect Dining Experience: Integrating Cow Behavior, Housing, and Feeding Management...... 233 Rick Grant, Ph.D., President William H. Miner Agricultural Research Institute

Common Myths About Feeding Fat to Lactating Cows...... No Paper Louis Armentano, Ph.D. University of Wisconsin - Madison CALIFORNIA ANIMAL NUTRITION CONFERENCE

Title & Author Page

Speaker Biographies ...... 264

2017 CANC Committee Member Biographies ...... 269

Past CANC Chairpersons...... 272

History of the California Animal Nutrition Conference...... 274

Student Abstracts for Poster Presentation...... 275 Potential for feeding program to impact gut health and inflammation in dairy cattle

Tanya F. Gressley Department of Animal and Food Sciences, University of Delaware

INTRODUCTION

The classic role of the dairy cattle nutritionist is to formulate rations that meet animal performance requirements. However, as the delicate interplay between animal nutrition and health continues to unfold, we are beginning to understand more ways that nutritional programs impact the health of the digestive tract and consequently the health and performance of the animal. The goal of this paper is to provide a brief overview of gut structures and to discuss the potential for nutrition to negatively or positively impact gut health. Sub-acute ruminal acidosis (SARA) and yeast feeding will be used as specific examples to illustrate negative and positive effects of different feeding programs on gut health, respectively.

THE DIGESTIVE EPITHELIUM

The rumen epithelium serves as a selective barrier, allowing for absorption of short chain fatty acids (SCFA) while preventing entry and colonization by bacteria. Structurally the rumen epithelium consists of four layers, the stratum corneum, stratum granulosum, stratum spinosum, and stratum basale. In the healthy rumen, bacteria are loosely associated only with the stratum corneum. Tight junction proteins that regulate the permeability barrier are expressed most heavily in the stratum granulosum and to some extent in the stratum spinosum (Graham and Simmons, 2005). Connections among the stratum granulosum, stratum spinosum, and stratum basale allow for the transport of SCFA from the rumen contents to the basal lamina (Graham and Simmons, 2005).

The intestinal mucosa is made up of different classes of epithelial cells. The cells in the greatest numbers are the columnar epithelial cells responsible for the absorption of dietary nutrients. Tight junctions between columnar epithelial cells are crucial for forming the physical barrier between the tissue and digesta. Interspersed among the columnar epithelial cells are a variety of specialized cells that help to protect the tissue from bacteria and toxins within the gut lumen. Goblet cells produce mucus that adheres to the luminal side of epithelial cells and provides an additional physical barrier. The mucus also houses a variety of bactericidal and bacteriostatic agents that prevent colonization by microbes that penetrate the mucus barrier. Many of these antimicrobial agents originate from Paneth cells that produce different compounds in response to stimuli from gut contents or surrounding cells. These antimicrobial agents include peptide defensin molecules as well as antimicrobial enzymes. M cells are another specialized class of epithelial cell that sample particulates from the gut lumen and present them to underlying immune cells.

GUT IMMUNE SYSTEM

Gut associated lymphoid tissue (GALT) consists of structures of white blood cells that are found throughout the digestive tract in close association with the epithelial cells. These range in size

1 from large Peyer’s patches to small isolated lymphoid follicles and consist of clusters of B and T lymphocytes interspersed with dendritic cells and phagocytes (Goto and Kiyono, 2012). Antigen presentation from M cells or dendritic cells causes B cells to be activated to IgA-secreting plasma cells. Secretory IgA then exits the columnar epithelial cells via transcytosis where it accumulates in the mucus to prevent attachment and translocation of target bacteria (Kamada et al., 2013). T cells in germinal centers within GALT are activated by receptor binding of microbial products and locally produced cytokines. In a healthy animal, tolerance of commensal gut microorganisms is facilitated by T cells with a regulatory phenotype that tend to suppress inflammatory responses by surrounding cells (Littman and Pamer, 2011).

Activities of lymphocytes and phagocytes within the healthy GALT respond to the microbiome to elicit appropriate responses: tolerance and local immunosuppression in response to commensal organisms and inflammation and immune activation in response to a microbial threat. Binding of bacterial components to pathogen recognition receptors such as toll like receptors (TLR) and NOD-like receptors is essential for homeostasis. Normal development of GALT structures is dependent upon functional pathogen recognition by these receptors. In addition to promoting proper GALT development, a symbiotic mix of commensal bacteria promotes mucus production and barrier function of the epithelium and inhibits colonization by competitive organisms (Kamada et al., 2013). The effects of the microbiome on GALT and mucosal functions are not only through direct interactions of microbial components with receptors but also through products of symbiotic microbes including short chain fatty acids (Brestoff and Artis, 2013).

Dysbiosis, an unhealthy shift in microbiome composition, can down-regulate these protective functions, stimulate mucosal inflammation, and potentiate colonization by pathogenic organisms. In humans, some disease states including inflammatory bowel disease are associated with a complete shift in intestinal population structure (Koboziev et al., 2014), and the shift in microbiome accompanying chronic inflammation diseases can drive intestinal T cells towards pro-inflammatory Th17 phenotypes (Littman and Pamer, 2011). Work in rodent models is beginning to demonstrate that chronic inflammatory diseases including metabolic syndrome, diabetes, and atherosclerosis are associated with shifts in intestinal microbiota (Caesar et al., 2012; Vieira et al., 2013). Although cause and effect are unclear, in certain instances transfer of healthy microflora to a sick individual can restore health and transfer of microbiota from a sick donor to a healthy recipient can cause disease. For example, mice lacking TLR5, a receptor that recognizes bacterial flagellin, developed metabolic syndrome and altered intestinal microbiome compared to wild-type mice (Vijay-Kumar et al., 2010). When their gut contents were transferred to healthy germ-free wild-type mice, the recipient mice developed characteristics of metabolic syndrome as well, indicating that the microbiome is important in both maintaining health and contributing to disease states. Such findings could be of relevance in the cow also, particularly in the transition period when cows experience some symptoms of metabolic syndrome including decreased insulin sensitivity, increased glucose tolerance, and increased mobilization of fatty acids.

DIET INDUCED INFLAMMATION IN DAIRY CATTLE – THE SARA EXAMPLE

The interaction between changes in gut microbiome and dairy cattle health is most well documented as it relates to rumen acidosis and sub-acute rumen acidosis (SARA). A switch to a

2 high grain diet or induction of SARA induces dramatic changes in the rumen fluid microbiome and in population structure of bacteria adhered to the rumen epithelium (Khafipour et al., 2009b; Chen et al., 2011). Sub-acute rumen acidosis-inducing diets also increase flow of fermentable carbohydrates to the intestines. This results in shifts in the intestinal microbiome as demonstrated by changes in fecal bacterial composition (Mao et al., 2012). These shifts in gastrointestinal bacterial communities in response to SARA are believed to be a key first step in the negative impacts of SARA on animal health and performance. In addition to the microbiome shift, acidosis also increases concentration of toxic and inflammatory compounds in the digesta (Ametaj et al., 2010; Li et al., 2012; Saleem et al., 2012) concurrent with a decrease in barrier function of rumen epithelium (Steele et al., 2011). Because the intestinal epithelium is composed of only a single layer of epithelial cells, systemic entry of bacteria or toxins in response to SARA may be more likely to occur in the intestinal mucosa than in the rumen. In fact, Khafipour et al. (2009a) found the timing of the presence of lipopolysaccharide in the blood following a SARA challenge suggested entry through the intestines instead of the rumen.

Shifts in the microbiome, accumulation of toxins in the digesta, and mucosal damage in response to SARA likely shift nearby GALT structures from an anti-inflammatory to a pro-inflammatory state, which, based on rodent models, may result in further damage to the mucosa and systemic inflammation. This may be a contributing factor to the increase in circulating acute phase proteins observed in response to grain-based SARA challenges (Plaizier et al., 2008). Systemic inflammation resulting from SARA likely contributes to the negative impacts of SARA on animal health.

MITIGATING GUT INFLAMMATION

Because SARA is one of the most recognized feeding problems that contribute to systemic inflammation, feeding strategies to minimize the risk of SARA should help to maintain gut health. Maintaining adequate NDF, particle size, and effective fiber and avoiding excessive starch or highly fermentable NFC are essential. Dietary buffers should be included in rations, and drier diets should be evaluated for their potential to contribute to sorting, which may induce SARA in some cows.

The permeability barrier function of the epithelium responds to changes in the animal or the digestive tract. For example, permeability is increased during oxidative stress or heat stress (Mani et al., 2012). Lactating and transition dairy cows are under considerable oxidative stress. Intracellular and extracellular antioxidants function to neutralize free radicals and prevent damage to cells and their membranes. Therefore, feeding to maintain adequate antioxidant status can help to ensure gut health. Nutrients with antioxidant activities include copper, manganese, and zinc which all serve as cofactors for superoxide dismutase, an enzyme responsible for converting superoxide into oxygen and hydrogen peroxide. Selenium also performs antioxidant functions primarily by acting as a cofactor for thioredoxin reductase enzymes that convert hydrogen peroxide to water. Dairy cattle selenium requirements are 0.3 mg/kg which is also the maximum supplementation level allowed (NRC, 2001). Though dietary selenium cannot be increased beyond this maximum, feeding selenium yeast instead of inorganic selenium provides a means to increase selenium absorption (Weiss and Hogan, 2005). Vitamins E and C also benefit gut and overall health through their antioxidant activities.

In addition to minerals and vitamins, other supplements may stimulate gut health. The effect of supplemental dietary fatty acids, in particular omega-3 fatty acids, on dairy cattle health is an area of active research. There is also some work evaluating the feeding of chicken egg IgY products from chickens vaccinated against enteric pathogens. Other supplements that can alter the gastrointestinal microbiome and potentially impact animal health include ionophores and essential oils. An area that has received significant attention is the potential use of probiotics and prebiotics, and the remainder of this paper will focus research related to feeding yeast.

Mitigating gut inflammation – yeast example

Feeding of prebiotics and probiotics to monogastric animals can shift the intestinal microbiome, alter activities of GALT, and improve animal health. Although determining those effects in ruminants is more complicated due to pregastric fermentation, it is likely that health benefits to feeding some prebiotics and probiotics can occur for dairy cattle as well.

Live yeast and yeast products have received a fair amount of study and have potential to be used as health promoting supplements in dairy cattle. Most studies have evaluated the effect of Saccharomyces cerevisiae, but there has been some work with other organisms including S. boulardii and Aspergillus oryzae. Studies indicate that feeding yeast supplements alters the rumen microbiome and can increase both fiber digestion and rumen pH (Chaucheyras-Durand and Durand, 2010; Pinloche et al., 2013). Yeast seem to function primarily as a prebiotic because similar changes in digestion occur whether yeast are alive or dead, although there may be a slight benefit to the live probiotic form (Oeztuerk et al., 2005; Oeztuerk, 2009). Work in monogastrics indicates that in addition to altering the intestinal microbiome, yeast supplementation can benefit host health through interaction of yeast carbohydrate moieties (primarily mannan oligosaccharides and β-glucans) with the gut mucosa. It is believed that these moieties can bind to pathogen receptors and, in so doing, alter functionality of the mucosa to increase barrier function and pathogen resistance (Munyaka et al., 2012).

In piglets, feeding of live S. boulardii reduced translocation of orally gavaged Escherichia coli across the gut mucosa and into the mesenteric lymph nodes (Lessard et al., 2009). In non- challenged broilers, feeding a commercial yeast derived product resulted in decreased mRNA levels of inflammatory cytokines in the ileum and cecum (Munyaka et al., 2012). In another broiler study, feeding of live S. boulardii increased goblet cell density in the jejunum, tight junction mRNA levels in the jejunum and ileum, and secretory IgA concentration in the jejunum (Rajput et al., 2013). Feeding of live yeast or yeast products to dairy cattle may have a similar ability to enhance animal health by increasing mucosal barrier function and altering GALT activity. Inclusion of S. cerevisiae into grain starter was shown to improve health of young calves (Magalhaes et al., 2008). In that experiment, scours was highly prevalent, and mortality was 12.1% in control calves and 7.5% in calves given the live yeast supplement. In a study using Holstein steers, we found that abomasal infusion of S. boulardii reduced fecal volatile fatty acid and pH changes following an abomasal oligofructose challenge, suggesting that S. boulardii improved the intestinal environment (Gressley et al., 2016). Finally, a study using transition cows fed cows 0, 30, 60, or 90 g/d of a combination live yeast and enzymatically hydrolyzed yeast product, and they found that increasing dose resulted in improved humoral immune

4 response to a model vaccination (Yuan et al., 2015). Interestingly, the moderate levels of yeast (30 and 60 g/d) also increased fecal IgA concentration, suggesting a direct impact of the supplement on gut health.

CONCLUSIONS

The gut is specialized to both absorb nutrients and to protect the animal from the microbes and toxins that exist within the digesta. Typically, gut immune cells are hyporesponsive, allowing for tolerance of commensal microorganisms and maintenance of the gut barrier function. Sub-acute ruminal acidosis is an example of a feeding situation that can disrupt this balance, leading to a breakdown of gut barrier function and localized and systemic inflammation. On the other hand, there exists potential for feeding strategies to improve gut health and barrier function. For example, probiotic and prebiotic supplements can stimulate shifts in the ruminal or intestinal microbiome that increase mucosal barrier integrity and boost systemic immunity.

REFERENCES

Ametaj, B. N., Q. Zebeli, F. Saleem, N. Psychogios, M. J. Lewis, S. M. Dunn, J. G. Xia, and D. S. Wishart. 2010. Metabolomics reveals unhealthy alterations in rumen metabolism with increased proportion of cereal grain in the diet of dairy cows. Metabolomics 6:583-594. Brestoff, J. R. and D. Artis. 2013. Commensal bacteria at the interface of host metabolism and the immune system. Nature Immunol. 14:676-684. Caesar, R., C. S. Reigstad, H. K. Backhed, C. Reinhardt, M. Ketonen, G. O. Lunden, P. D. Cani, and F. Backhed. 2012. Gut-derived lipopolysaccharide augments adipose macrophage accumulation but is not essential for impaired glucose or insulin tolerance in mice. Gut 61:1701-1707. Chaucheyras-Durand, F. and H. Durand. 2010. Probiotics in animal nutrition and health. Benef. Microbes 1:3-9. Chen, Y. H., G. B. Penner, M. J. Li, M. Oba, and L. L. Guan. 2011. Changes in bacterial diversity associated with epithelial tissue in the beef cow rumen during the transition to a high-grain diet. Appl. Environ. Microb. 77:5770-5781. Goto, Y. and H. Kiyono. 2012. Epithelial barrier: an interface for the cross-communication between gut flora and immune system. Immunol Rev 245:147-163. Graham, C. and N. L. Simmons. 2005. Functional organization of the bovine rumen epithelium. Am J Physiol-Reg I 288:R173-R181. Gressley, T. F., K. A. Davison, J. Macies, C. Leonardi, M. M. McCarthy, L. M. Nemec, and C. A. Rice. 2016. Effect of abomasal carbohydrates and live yeast on measures of postruminal fermentation. J Anim Sci 94:284-296. Kamada, N., S. U. Seo, G. Y. Chen, and G. Nunez. 2013. Role of the gut microbiota in immunity and inflammatory disease. Nat. Rev. Immunol. 13:321-335. Doi 10.1038/Nri3430. Khafipour, E., D. O. Krause, and J. C. Plaizier. 2009a. A grain-based subacute ruminal acidosis challenge causes translocation of lipopolysaccharide and triggers inflammation. J. Dairy Sci. 92:1060-1070. Khafipour, E., S. Li, J. C. Plaizier, and D. O. Krause. 2009b. Rumen microbiome composition determined using two nutritional models of subacute ruminal acidosis. Appl. Environ. Microbiol. 75:7115-7124.

5 Koboziev, I., C. R. Webb, K. L. Furr, and M. B. Grisham. 2014. Role of the enteric microbiota in intestinal homeostasis and inflammation. Free Radical Bio. Med. 68:122-133. Lessard, M., M. Dupuis, N. Gagnon, E. Nadeau, J. J. Matte, J. Goulet, and J. M. Fairbrother. 2009. Administration of Pediococcus acidilactici or Saccharomyces cerevisiae boulardii modulates development of porcine mucosal immunity and reduces intestinal bacterial translocation after Escherichia coli challenge. J. Anim. Sci. 87:922-934. Li, S., E. Khafipour, D. O. Krause, A. Kroeker, J. C. Rodriguez-Lecompte, G. N. Gozho, and J. C. Plaizier. 2012. Effects of subacute ruminal acidosis challenges on fermentation and endotoxins in the rumen and hindgut of dairy cows. J. Dairy Sci. 95:294-303. Littman, D. R. and E. G. Pamer. 2011. Role of the commensal microbiota in normal and pathogenic host immune responses. Cell Host Microbe 10:311-323. Magalhaes, V. J., F. Susca, F. S. Lima, A. F. Branco, I. Yoon, and J. E. Santos. 2008. Effect of feeding yeast culture on performance, health, and immunocompetence of dairy calves. J. Dairy Sci. 91:1497-1509. Mani, V., T. E. Weber, L. H. Baumgard, and N. K. Gabler. 2012. GROWTH AND DEVELOPMENT SYMPOSIUM: Endotoxin, inflammation, and intestinal function in livestock. J. Anim. Sci. 90:1452-1465. Mao, S. Y., R. Y. Zhang, D. S. Wang, and W. Y. Zhu. 2012. The diversity of the fecal bacterial community and its relationship with the concentration of volatile fatty acids in the feces during subacute rumen acidosis in dairy cows. BMC Vet. Res. 8. Artn 237 Munyaka, P. M., H. Echeverry, A. Yitbarek, G. Camelo-Jaimes, S. Sharif, W. Guenter, J. D. House, and J. C. Rodrigez-Lecompte. 2012. Local and systemic innate immunity in broiler chickens supplemented with yeast-derived carbohydrates. Poultry Sci. 91:2164- 2172. NRC. 2001. Nutrient Requirements of Dairy Cattle. 7th rev. ed. ed. Natl. Acad. Sci., Washington, DC. Oeztuerk, H. 2009. Effects of live and autoclaved yeast cultures on ruminal fermentation in vitro. J. Anim. Feed Sci. 18:142-150. Oeztuerk, H., B. Schroeder, M. Beyerbach, and G. Breves. 2005. Influence of living and autoclaved yeasts of Saccharomyces boulardii on in vitro ruminal microbial metabolism. J. Dairy Sci. 88:2594-2600. Pinloche, E., N. McEwan, J. P. Marden, C. Bayourthe, E. Auclair, and C. J. Newbold. 2013. The effects of a probiotic yeast on the bacterial diversity and population structure in the rumen of cattle. Plos One 8. ARTN e67824 Plaizier, J. C., D. O. Krause, G. N. Gozho, and B. W. McBride. 2008. Subacute ruminal acidosis in dairy cows: the physiological causes, incidence and consequences. Vet. J. 176:21-31. Rajput, I. R., L. Y. Li, X. Xin, B. B. Wu, Z. L. Juan, Z. W. Cui, D. Y. Yu, and W. F. Li. 2013. Effect of Saccharomyces boulardii and Bacillus subtilis B10 on intestinal ultrastructure modulation and mucosal immunity development mechanism in broiler chickens. Poultry Sci. 92:956-965. Saleem, F., B. N. Ametaj, S. Bouatra, R. Mandal, Q. Zebeli, S. M. Dunn, and D. S. Wishart. 2012. A metabolomics approach to uncover the effects of grain diets on rumen health in dairy cows. J. Dairy Sci. 95:6606-6623. Steele, M. A., J. Croom, M. Kahler, O. AlZahal, S. E. Hook, K. Plaizier, and B. W. McBride. 2011. Bovine rumen epithelium undergoes rapid structural adaptations during grain- induced subacute ruminal acidosis. Am J Physiol-Reg I 300:R1515-R1523.

6 Vieira, A. T., M. M. Teixeira, and F. S. Martins. 2013. The role of probiotics and prebiotics in inducing gut immunity. Front. Immunol. 4:445. Vijay-Kumar, M., J. D. Aitken, F. A. Carvalho, T. C. Cullender, S. Mwangi, S. Srinivasan, S. V. Sitaraman, R. Knight, R. E. Ley, and A. T. Gewirtz. 2010. Metabolic syndrome and altered gut microbiota in mice lacking toll-like receptor 5. Science 328:228-231. Weiss, W. P. and J. S. Hogan. 2005. Effect of selenium source on selenium status, neutrophil function, and response to intramammary endotoxin challenge of dairy cows. J. Dairy Sci. 88:4366-4374. Yuan, K., L. G. D. Mendonca, L. E. Hulbert, L. K. Mamedova, M. B. Muckey, Y. Shen, C. C. Elrod, and B. J. Bradford. 2015. Yeast product supplementation modulated humoral and mucosal immunity and uterine inflammatory signals in transition dairy cows. J Dairy Sci 98:3236-3246.

7 Tanya Gressley Tanya Impact Gut and Gut Health Impact Inflammation Cattle in Dairy Inflammation Potential for Feeding Program to to Program Feeding for Potential Department Department of Animal and Sciences Food

8 Outline inflammation as an example as an example east east SARA SARA Y – – Overview of gut morphology and gut health gut and morphology gut of Overview healthGut and manipulation Dietary of gut health • • •

9 m 50 μ SB SS SG SC Rumen Structure Rumen m μ 100 Healthy rumen papillus Healthy

10 Large Intestine Intestinal Structure Intestinal

11 cells Intestinal Protection Intestinal Goblet cells Goblet M cells Paneth • • • Tight junctions cellsbetween animal from protect to Cells specialized pathogenic microbes and their toxins – – Animals are protected from toxins and toxins from protected Animals are via: intestines in their microbes •

12 Suzuki 2013 Suzuki Tight Junctions Tight Block entry of pathogens and dietary antigens – Tight junction proteins link intestinal mucosa intestinal link junction proteins Tight cells •

13 Small Intestine Small Intestine Large Goblet Cells Goblet Johansson et al. 2013 al. Johansson et Goblet cells secrete mucins cellsGoblet secrete in looseand adhered found the mucus protects that epithelium intestinal •

14 et al. 2013 et Kurashima Cells Paneth cells secrete: cells ntimicrobial peptides A ntimicrobial (AMPs) Immunoregulatory molecules Defensins Lysozymes Cathelicidins – – – – – Paneth in (stored AMPs granules) • •

15 et al. 2013 et Mabbott (M) Cells Microfold associated associated M cells transport M cells transport macromolecules, and antigens, to microorganisms gut - lymphoid (GALT) tissue •

16 cells Paneth release antibacterial into compounds mucus Bacteria and antigens Bacteria M cells present and bacteria to antigens immune cells Intestinal lumen Intestinal Mucus and tight tight Mucus and provide junctions barrier physical Goblet cells mucus produce Feed particles Feed Nutrients Enterocytes nutrients deliver blood to

17 propria Epithelial Immune System Immune Epithelial associated lymphoid (GALT) tissue associated Microstructures Microstructures of blood white cells that lie just beneath the epithelium in the lamina Respond to gut contents by maintaining maintaining by gut contents Respond to (e.g. of commensal homeostasis presence bacteria) or immuneactivating response (e.g. organisms) of pathogenic presence cell of cycles epithelial Regulate cells and junctions “tightness” of tight Sub - – – – Gut - •

18 . Pearson et al. et Pearson 2012. Trends Immunol 33:289 Associated Lymphoid Tissue Lymphoid Associated - Gut

19 Circulatory vessel Circulatory B cells Intestinal lumen Intestinal T cells Dendritic cell Macrophage

20 IgA Immunoglobulin A Immunoglobulin Bacteria Can bind and bacteria anddeactivate antigens specific are that IgA different many B cells produce single aor antigen to organism – – Immunoglobulin by (IgA)A is produced (plasma B cells cells) activated •

21 B cells Intestinal lumen Intestinal

22 . 33:289 Dysbiosis Pearson et al. 2012. Trends Immunol Trends 2012. al. et Pearson or pathogenic organism organism or pathogenic Response to to Response mucosal space - Inflammation of recruitment and Activation immune cells Damage to epithelium and pathogens of organisms Passage to sub – – – – Commensal in bacteria gut inflammation suppress Dysbiosis in: results challenge • •

23 Studies Monogastric dysbiosis production and tight junction integrity, production and junctiontight integrity, promotes systemic and local inflammation, and and inflammation, and local systemic promotes animals induce sicknessin healthy can are states disease inflammatory Chronic with associated Crosstalk between microbes, intestinal cells, and intestinal microbes, between Crosstalk epithelium healthy for is cells essential GALT microbes commensal mix of A “healthy” suppresses and production mucin promotes inflammation An “unhealthy” mix reduces of gut microbes mucin Findings from from Findings • • • •

24 following Papillus challenge SARA VFA ↑ pH (SARA) ↓ Bacteria Toxins Fermentable CHO Fermentable ↑ Bacteria Growth Rate ↑ to SARA (Inflammation) SARA to ↑ Damage to GI Epithelium to Damage Acute Rumen Acidosis Rumen Acute Systemic Immune Response Immune Response Systemic Shifts in Bacteria Populations Bacteria in Shifts Sub - m μ 100 Acute RumenAcute Acidosis (SARA) Sub - Healthy rumen papillus Healthy

25 Reg. I. 300:R1515 Reg. Univ. of Guelph, Univ. of Manitoba, UNC of Manitoba, Guelph, of Univ. Univ. - al. et Physiol. Steele 2011. Am. J. Rumen Epithelium Response to SARA to Response Rumen Epithelium

26 ) ) ) . 75:7115 induced SARA induced SARA induced SARA - - - Firmicutes induced SARA ( - Bacteriodetes ( et al. 2009. Appl. Appl. 2009. al. et Firmicutes ( Microbiol ) Proteobacteria ( elsdenii bovis Severe grain Severe Severe grain Severe grain Mild Alfalfa pellet Alfalfa E. coli coli E. M. S. Prevotella ↑ ↑ ↑ Manitoba of University Khafipour Environ. ↑ Severity of SARA and degree of of degree and SARA of Severity correlated inflammation highly abundance coli E. with Rumen Fluid Bacteria Shifts Shifts SARA during Fluid Rumen Bacteria

27 sp. sp., and and sp., sp., Treponema Ruminobacter Lachnospiraceae detected only Were the fed when high grain diets . 77:5770 Microbiol University of Alberta of University Appl. al. Chen 2011. et Environ. Papillae Adherent Bacteria Shifts during High Grain Feeding Grain High during Shifts Bacteria Adherent Papillae 75% 75% theseof fed were heifers diet 97% hay These heifers were were heifers These or 72% fed grain diet 89% grain

28 et et al. 2012. Methylamine Putrescine Ethanolamine • • • Saleem JDS 95:6606 Other biologically active Other active biologically increase that compounds during SARA: Study 3 Study Grain-based SARA Grain-based Study 2 Study Control Study 1 Study et al. 2009. JDS et 92:1060; mean of 3 time points et al. 2007. JDS 90:856; reverse log transformed JDS 90:856; reverse 2007. al. et 0 Rumen Fluid Endotoxin (LPS) Fluid Endotoxin Rumen Gozho Khafipour

50,000

200,000 150,000 100,000 Rumen fluid LPS, EU/mL LPS, fluid Rumen Study 1: 1: Study Study 2: 2: Study Study 3: Li et al. 2012. JDS 95:294 2012. al. et Li 3: Study

29 12 h 6 h 0 h Diarrhea, frothy feces, increased fecal particle size, size, particle fecal increased feces, frothy Diarrhea, mucin casts SARA Impacts on the Intestines the on Impacts SARA – Excessive carbohydrate fermentation in the fermentation carbohydrate Excessive in the intestines changes by rumen mirrored of SARA indicators Fecal • •

30 Mao et al. 2012. BMC Vet. al. 2012.Mao BMC Vet. et 8:237 Res. SARA Control Fecal Bacteria Shifts During During SARA Shifts Bacteria Fecal

31 b ST4 5,324 a b b induced SARA 17,326 30,715 - ST1 6,415 Alfalfa c OL4 SARA c 21,345 b b 93,154 induced 118,522 - OL1 7,341 Grain a a a 2,449 Control Control 12,832 10,405 LPS in the Large Intestine Large the in LPS Li et al. 2012. JDS 95:294 2012. al. et Li Gressley et al. 2016. JAS 94:284 et al. 2016. JAS Gressley 42 h Feces LPS (EU/g) Rumen (EU/mL) Rumen LPS Abomasal oligofructose (OL) or starch (ST) challenge (ST) or starch (OL) oligofructose Abomasal SARA feeding challenge feeding SARA Feces LPS (EU/g), average average (EU/g), LPS Feces over

32 Heart Liver release phase of release acute Liver and inflammatory proteins generates cytokines inflammation systemic If liver is damaged, bacteria If bacteria liver is damaged, enter can antigens or circulation systemic Liver Intestines Rumen Bacteria or antigens or antigens Bacteria penetrate that liver to flow mucosa Inflammatory Response to SARA to Response Inflammatory

33 induced SARA, binding protein (LBP), binding protein - 1 acid glycoprotein, 1 acid glycoprotein, - induced SARA causes induced causes SARA α ( Hp ), LPS Liver Acute Phase Proteins Phase Acute Liver Produced by liver and otherProduced liver tissuesby in response to an inflammatory trigger – and serum amyloid A (SAA) A amyloid and serum haptoglobin inflammation Main ones are response, the inflammatory moderate They and tissue repair recovery, - grain to in response Produced - grain indicating • • •

34 Exp. 4 Exp. Control SARA Exp. 3 Exp. Exp. 2 Exp. Exp. 1 Exp. 3 2 1 0

2.5 1.5 0.5 Haptoglobin (mg/mL) Haptoglobin Exp. 4 Exp. et al. 2008. Vet. J. 176:21 J. al. et 2008. Vet. Exp. 3 Exp. Plaizier Exp. 2 Exp. Control SARA Exp. 1 Exp. 0

SARA and Blood Acute Phase Proteins Phase and Blood Acute SARA

600 500 400 300 200 100

g/mL) μ ( A Amyloid Serum

35 Summary thus far thus Summary In healthy animals, gut structures and the gut and the animals, gut structures In healthy inflammation suppress to interact microbiome animal health maintain and example, the SARA by illustrated As in result disruptions can in gut homeostasis and systemic tissue damage local performance inflammation → reduced • •

36 , , NFC ) Ondarza de Penner fermentable fermentable and , Grant, Linn) , Grant, Beauchemin Ondarza 21% forage NDF ( 21% forage (de 28% starch ( depending availability on NFC, 44% Grant, Linn) Grant, 19 - 23 - 30 - • • • Mitigating Gut Inflammation Mitigating Adequate NDF, particle size, and effective fiber and effective particlesize, NDF, Adequate and highly starch excessive Avoid moisture Adequate Include buffers – – – – Prevent SARA Prevent •

37 50 50 - , 0.3 ppm Se, 40 Se, Mn , 0.3 ppm (Weiss) 42 ppm ppm 42 prefresh 15 15 - 33 Cu, ppm Mitigating Gut Inflammation Mitigating ppm Zn (Weiss) Zn ppm - 12 Vitamin E: 500 IU/d lactating, 1,000 IU/d dry, 500 E: 1,000 IU/dVitamin lactating, IU/d dry, more potentially – – Ensure adequate antioxidant status antioxidant adequate Ensure stress heat Avoid • •

38 Yeast • Mitigating Gut Inflammation Mitigating Omega 3 fatty acid supplements 3 fatty Omega Ionophores IgY Essential oils Prebiotics or probiotics – – – – – Supplements with potential benefit potential with Supplements •

39 can bind can glucans - β activity Yeast oligosaccharides and ncreased ncreased barrier function I mucusIncreased production resistance pathogen Increased immunomodulating • • • pathogen receptors on mucosa on mucosa receptors pathogen binding moiety yeast to Response Increases fiber digestion and pH rumen fiber digestion Increases efficiency digestive Increases SARA help prevent May Mannan – – – – – Prebiotic and probiotic activitiesand probiotic Prebiotic Gut • •

40 S. boulardii challenge acidilactici boulardii E. coli Saccharomyces Saccharomyces Piglet Study, Study, Piglet et al. 2009. JAS. JAS. 2009. al. et Lessard - 934 87:922 (SCB) of transfer Measured mesenteric to bacteria lymph nodes following oral an Dietary supplements supplements Dietary (ABT), antibiotics were Pediococcus cerevisiae (PA) or or (PA) • •

41 ) Bs B10 ( (Sb) Ctr ) subtilis Broiler Study, S. boulardii Study, Broiler boulardii Bacillus S. Control ( Control Rajput et al. 2013. Poultry Sci. Sci. Poultry 2013. al. et Rajput - 965 92:956

42 ) Bs B10 ( subtilis boulardii Bacillus S. (Sb) 965 boulardii Ctr ) S. Broiler Study, Study, Broiler Control ( Control - 92:956 Sci. Poultry 2013. al. et Rajput

43 × in steers in (10 g/d of 2 every 6 h beginning every boulardii boulardii water S. S. /g) cfu 10 Abomasal Designed induce to upset digestive Abomasal 10 – – – at 24 h at All steers received 4 abomasal received steers All pulse doses of 0.25 g/kg BW oligofructose Treatments • •

44 abomasal = 0.25 g/kg

BW oligofructose

mM Lactate, 20 18 16 14 12 10 8 6 4 2 0 72 66 60 54 Hour 48 in steers 42 S. boulardii total VFA total S. boulardii S. lactate boulardii 36 30 24 18 boulardii 12 S. 6 0 Control total VFA Control lactate 0

90 80 70 60 50 40 30 20 10

100 mM , VFA+Lactate Total Gressley et al. 2016. JAS 94:284 et al. 2016. JAS Gressley

45 et al.,et Magalhães 91:1497 JDS, 2008, and Calves and Yeast Yeast Control starter grain starter Control V XP) (Diamond culture yeast 2% with grain Starter – – 512 calves, 2 treatments: 512 calves, •

46 to 42 d postpartum with 0, 42 d postpartum to ) Celmanax Yeast and Cows Yeast Fed from 21 d prepartum from Fed 30, 60, or 90 g/d yeast culture plus enzymatically or 60, plus30, enzymatically 90 g/d culture yeast ( yeast hydrolyzed – 40 transition cows 40 transition •

47 Take Messages Home Take SARA example SARA example Yeast – – Gut tissues are complex and changes in digesta in changes and complex are tissues Gut host the impact microbiome and digestive impactsThe diet gut health and local inflammation systemic to tool useful a provide may strategies Feeding gut the fortify microbiome, gut the alter inflammation epithelium, and reduce • • •

48 Thank You! Thank

49 WNF 2016 on intestinal on intestinal Associate Professor Associate and function in pigs Nicholas Gabler Effects of stress and and of stress Effects entodoxins integrity integrity Department of Animal Science

50 Department of Animal Science Growth is Potential Growth Reduced Metabolic Altered Demand Reduced Efficiency Feed intake Growth rates Wellbeing Mortality Profitability • • • • • Intestinal function, Stress, Immune and and Stress, Immune function, Intestinal impact challenges Inflammatory Introduction •

51 Department of Animal Science …) and climate stress etc Why focus on the on the Why focus GI tract? Reduced pig performance and wellbeing Important for digestionImportantand absorption Intestinal barrier necessarypreventingpathogen for translocation and maintaining homeostasis due to can GI tractintegrity in pigsbe compromised weaning stress, enteric disease, nutrition Large immune organ and inflammation to hypoxia Susceptible (mycotoxin o o o o o 

52 Department of Animal Science The GI system reflects a complex reflects a complex cooperative and system GI The organs of various network specialization Cellular and redundant interactive Highly efficient, and liver set them tract circulatory GI The of the features organs from other apart governed are tractby the GI Many functions of the enteric nervous system Function of the GI tract • • • • •

53 Department of Animal Science Motility Secretion Digestion Absorption Excretion defense Host 1. 2. 3. 4. 5. 6. Key processes of the GIT Function of the GI tract •

54 Department of Animal Science Fats consumed as TAG but absorbed as MAG and FFA Protein consumed as proteins and large peptides but absorbed AA andas small peptides CHO is present in the diet as starch, disaccharidesand monosaccharide but absorbed as monosaccharide • • • The primary function of the GI tract is to transfer tract GI primary The function of the the foods we eat and nutrients, water fromelectrolytes the body’s into internal environment Also contributes to appetite control The act of eating does not automatically the make preformed organic molecules food available in to the body of fuel building cells as a source blocks Organization of the Organization GI tract • • •

55 Department of Animal Science Digestion and Digestion Absorption

56 4278 - J M et al. Mol. Biol.Cell 2007;18:4261 Department of Animal Science Halbleib ) ) ) GJ) (D junctions (TJ junctions ( - protein interactions Desmosomes Desmosomes Adhesion junctions (AJ Tight Gap Gap • • • • Adhesion complexes form the epithelial barrier Intestinal Organization Barrier Intestinal o Protein Polarized epithelial cells canbe This barrier stress,compromised due to inflammation and damage Secretory products Secretory products o o o o

58 the TJthe actinomyosin , junctional (ZO) proteins, which in claudins , , Department of Animal Science perijunctional occludin occludens ) contribute to TJ formation. ) contribute ring permits the cytoskeletal the ring permits regulationcytoskeletal of ring is regulated by chainkinase ring is regulated by light the myosin tricellulin acinomyosin actinomyosin of the the of interaction with the the with interaction transmembrane families families transmembrane of proteins ( intracellular domainsproteins of interact with these transmembrane the to vital is cytoskeleton actin with the TJ proteins of interaction (MLCK) which phosphorylates chain. light phosphorylates which the myosin (MLCK) TJ Contraction barrier integrity. barrier integrity. • • adhesion molecule (JAM) and Four The cytosolicscaffolding proteins like zonula turn anchor proteins the to transmembrane the ring. The maintenance of TJ structure and function. • • •

59 3682 - 2008;121:3677 Sci localization proteins. M S , Matter , Matter Cell M S K J Department of Animal Science Balda occludin regulates occludin three types three selectivity:of of 2, 3, 4, 5, 7, 8, 12 and 15 are expressed in the , Anion selectivity Cation selectivity selectivityW ater 1 permeability i. ii. iii. considered to be the primary sealforming ~27 family ~27 family member proteins Are Claudin They can exhibit Transmembrane spanning P hosphorylation and TJ intestines. • • • • • • Claudin Occludin Tight Junction Tight Proteins • •

60 transport luminal cells and other - Department of Animal Science cells - M The transcellular route b) c) antigens and bacteria toward the underlying immune cells which are the actors of the immune ( response ( across M epithelial cells exhibits two classical pathways depending on the particle's size. Mechanisms of transcellular permeability • •

61 Department of Animal Science Increased intestinal permeability to pathogens and toxins (i.e. mycotoxinsendotoxin) and • Reduced intestinal integrity Reduced appetite Reduced function A compromised intestine reduces reduces intestine A compromised health and performance • • •

62 522 532 - - 514 523 Department of Animal Science Sci . 94:2: Sci . 94:2: Anim Anim GROWER PIG - GROWER INFECTION ON - INFECTION PERFORMANCE AND AND PERFORMANCE Schweer et al, (2016) J Schweer et al, (2016) J INTESTINAL FUNCTION INTESTINAL NURSERY VIRUS CO THE IMPACT PRRS AND PED AND IMPACT PRRS THE

63 21 dpi Department of Animal Science - collections at 21dpi 14 dpi P6 PFU/dose 7 inoculation PEDV PEDV digesta - 20 dpi cloned with with the plaque isolate Intragastric (USA/Iowa/18984/2013) at a rate of 10 of at a rate 7 dpi genomic PRRSV 2 0 dpi Intranasal and and Intranasal adaptation - 5 dpi equivalence of intramuscular inoculation intramuscular inoculation with saline or saline 10 with 16 kg BW pigs Body weights and feed intake recorded weekly Blood samples collected weekly Total tract fecal collections 17 Euthanized pigs for tissueand Methodology • • • • •

64 % 3.39 1.33 5.00 0.30 0.55 0.35 2.43 1.00 0.94 0.50 0.19 0.22 0.02 0.40 60.93 30.00 Department of Animal Science 2000 phosphate 21% Optiphous /kg oil Ingredient Stable Mcal Mcal /kg , Methionine - Threonine - ME, NE % SID Lysine, DDGS Soybean Monocalcium DL SBM Salt Mineral premix & Vitamin Corn Lime Lysine L Heat Titanium dioxide Titanium Calculated Diet

65 value 0.10 0.64 0.36 0.003 - P 0.14 0.16 0.31 0.36 SEM + 1.4 1.5 4.9 3.4 PEDV Department of Animal Science PRRSV PRRSV PPRSv + neg neg neg neg PEDV PEDv ISH IHC 1.1 1.2 5.2 3.9 PRRSV neg neg neg neg titer Control PRRS titer EIA QPCR only 3 1 dpi dpi dpi Clinical Diagnostic 21 14 14 21 dpi Log+ PEDv ISH IHC PRRSX Parameter

66 Department of Animal Science What is the impact on growth performance?

67 5 30% 50% - - 20% 0.003 0.002 0.001 0.005 0.095 0.019 0.189 0.032 - <0.001 Infection 4 PED 0.076 0.506 0.861 0.001 0.008 0.441 <0.001 <0.001 <0.001 3 value - P ADG reduced 20 ADFI reduced 10 reduced GF 10 PRRS 0.003 0.083 0.026 1.000 0.006 • • • <0.001 <0.001 <0.001 <0.001 Department of Animal Science 0.002 0.001 0.020 0.003 0.001 0.005 <0.001 <0.001 <0.001 Overall infected (PRP) - 0.04 0.03 0.05 0.03 0.04 0.07 0.05 0.04 0.02 SEM 2 c c b b b b b ab 0.73 0.31 0.34 0.41 0.76 0.20 0.67 0.47 PED 0.55 PRRS + 2 a a a b a bc ab ab 0.92 0.61 0.94 0.65 0.35 0.56 PED 0.39 0.88 0.51 2 b b b a a a b a (Control) 0.78 0.35 0.67 0.53 0.63 1.02 0.62 0.44 0.56 PRRS naïve 1 virus a a a a a ab a a 0.66 1.04 0.61 1.22 0.95 0.63 0.62 0.54 0.65 Control healthy, ; treatment per Performance < < 0.05 represents treatment differences pens 21 d Performance 3 G:F G:F P ADG, kg ADFI, kg ADFI, kg ADG, kg 14 d Performance 21 d Performance G:F ADFI, kg ADG, kg n= n=6 pens per treatment; PRRSV infected (PRRS), PEDV infected (PED), co contrast forPRRSVOrthogonalvs naïve PRRSV infected contrast forPEDVvs Orthogonal naïve PEDV infected challenged vs virus naïve virus contrastof Orthogonal 15 - Parameter 0 - 0 - 1 2 3 4 5 a,b,c

68 Department of Animal Science What is the impact on intestinal morphology and function?

69 villus Score = 4.42,Score Lesion Moderate atrophyand fusion, inflammatory mild infiltrates Department of Animal Science atrophy Score = 2.6 Score Lesion Mild villus some and fusion , inflammatory infiltrates morphological Score = 0.1, Lesion= 0.1, Score No changes Score = 0 Score morphological Lesion changes No Total Histological Disease Activity Scores Disease Histological Total Jejunum Ileum Duodenum

70 score the histology Department of Animal Science 003) Performance was negatively correlated with disease activity 0. 0.013) 0.0001) GF 1.00 0.82 0.54 - 0.53 21 dpi performance 21 dpi performance ( P = ( P = ( P = - 0.103) 0.0001) 0.91 1.00 - 0.37 ADFI ( P = ( P = 0.015) 1.00 ADG - 0.52 ( P = 1.00 Score GF ADG Correlations between between Correlations 0 activity scores disease and histological ADFI Score Pearson Correlation Coefficients Correlation Pearson

71 4 0.811 0.926 0.137 0.103 0.120 0.196 0.656 0.795 0.938 0.084 Infection 3 0.032 0.647 0.120 0.546 0.199 0.242 0.032 0.001 0.019 chambers PED <0.001 2 value - P Ussing 0.150 0.219 0.036 0.035 0.148 0.581 0.235 0.024 0.002 <0.001 PRRS modified 0.064 0.446 0.092 0.064 0.009 0.023 0.033 in <0.001 <0.001 <0.001 Overall (PRP) Department of Animal Science 2.54 0.50 34.1 37.2 0.06 1.28 0.48 0.34 0.74 1.17 SEM conducted b b b c b b infected=PRP - 171 388 1.7 1.4 4.2 13.4 12.8 PED 2.82 0.88 0.52 Co PRRS+ ; permeability b a ab b b ab chambers 77 265 0.6 2.4 h 12.1 12.7 PED 1.8 1.84 3.08 serosal 1.44 to per a Ussing a a protein infected=PED ab a a g 98 370 1.1 4.5 21.7 13.1 2.08 13.5 1.74 protein PRRS per 10.01 mucosal PEDV ; modified mg min protein in ab kDa b b b a b g 4 per . per 2.2 39 4 329 1.0 6.6 11.9 15.5 per 0.97 0.75 4.45 Control mol µ min conducted liberated dextran i infected=PRRS per P , AU 6 glucose, mol mol µ µ = PRRSV = , AU ; 6 , AU 6 measurement , AU liberated 8 6 - isothiocyanate 9 resistance activity = activity vivo , AU 5 treatment ex transport per 7 activity 7 7 ATPase - ATPase - PRRS and PED differentially alter intestinal intestinal alter PED differentially PRRS and function + + Fluorescein < 0.05 < pigs , /K /K 4 + 6 + P n= contrast forPRRSVOrthogonalvs naïve PRRSV infected contrast forPEDVvs Orthogonal naïve PEDV infected challenged vs virus naïve virus contrastof Orthogonal FD Normalized Enzyme Aminopeptidase Na Sucrase Na Transepithelial Lysine transport Lysine Lactase Maltase Glutamine Glutamine Aminopeptidase Parameter Ex vivo FD4 1 2 3 4 5 6 7 8 9 a,b,c Glucose transport

72 5 0.001 0.174 0.045 0.730 0.769 0.137 0.218 <0.001 Infection 4 0.010 0.004 PED 0.291 0.430 0.042 0.073 <0.001 <0.001 value - 3 P 0.234 0.462 0.643 0.100 0.534 0.641 0.409 0.596 PRRS infected (PRP) - infected Department of Animal Science 0.033 0.014 0.423 0.651 0.208 0.305 <0.001 <0.001 Overall 4.11 3.74 2.45 3.63 0.90 0.78 0.90 1.90 SEM 2 b b b b 65.1 66.8 69.0 48.8 80.7 89.2 76.8 74.9 PRP 2 b b ab ab (Control) 62.0 64.5 71.2 50.9 81.8 77.3 90.3 80.1 PED 2 naïve a a a a virus 70.3 69.9 75.8 57.4 87.5 92.7 86.4 84.7 differences PRRS 1 a a a ab healthy, ; 68.0 68.7 77.7 59.3 treatment 87.2 88.7 85.9 92.4 Control treatment represents per Matter matter Energy 05 . Apparent Digestibility Apparent 0 matter pens matter < 3 P n= n=6 pens per PRRSV treatment; infected (PRRS), PEDV (PED), infectedco infected PRRSV vs naïve PRRSV for contrast Orthogonal infected PEDV vs naïve PEDV for contrast Orthogonal challenged virus vs naïve virus of contrast Orthogonal Dry Dry Organic Gross Gross Nitrogen acids Nitrogen Organic Dry Dry Total amino Total Parameter ATTD 1 2 3 4 5 a,b AID

73 Department of Animal Science Lesser impact of AID • PEDV reduces apparentof reducesPEDV total tract digestibility nutrients and energy Enteric PEDV health challengesmay differ from systemic (i.e. PRRS) These data may be confounded by feed intake differences Contribution of endogenous losses unknown Apparent Digestibility Apparent • • • •

74 Department of Animal Science ENDOTOXIN

75 Whitfield (2002) and Department of Animal Science Raetz cascades of local of local and systemic inflammation bacterial endotoxin plays an important role in derived derived from negative gram bacteria such as E. potent initiator potent initiator of immune Salmonella derived derived - Activates receptor mediated receptor mediated Activates inflammatory processes Gut the development • • What is endotoxin? Endotoxin is Endotoxin Incredibly coli and and coli • •

76 Department of Animal Science “Leaky gut” Passive diffusion Receptor mediated(TLR4) endocytosis duringMediated fat absorption transport Micelle Paracellular transport through tight junctions transport enterocyte through the Transcellular . . . . 1. 2. How does enteric derived endotoxin enter circulation?

77 Department of Animal Science Increased intestinal permeability to pathogens and toxins (i.e. mycotoxinsendotoxin) and • Reduced intestinal integrity Reduced appetite Reduced function A compromised intestine reduces reduces intestine A compromised health and performance • • •

78 12 hours 12 - BW) challenge BW) et al., et al., 1997 fold increase in plasma plasma in increase fold - (PUN) at 8 at (PUN) g LPS/kgg Webel μ Department of Animal Science (5 the three the response to LPS Lipopolysaccharide nitrogen urea Time course evaluation of evaluation course Time Injection in nursery pigs nursery in Note • • • Classical innate immune

79 b 101.4 a ADG g/d 122.5 b Department of Animal Science 75.9 a Lean g/d 93.2 Repeated LPS b 3.8 about endotoxins? about LPS for 8 days a Saline Fat g/d 7.3 b 0.9 a 5 weekold broilers repeatedly challenged with BMC g/d 1.2 Why do we care Why do we care

80 (2003) cells Kimball, Kimball, S. R. et al. enteroendocrine 1 - Department of Animal Science (2007). Murine STC M et al. dysfunction catabolism Bogunovic of intestinal diabetes tissue catabolism II II Inhibits Inhibits skeletal muscle protein synthesis Lipolysis and protein Metabolic diseases/dysfunction Type Atherosclerosis • • • • • Development Peripheral Disease Suppresses appetite Why do we care ? Why do we care • • • •

81 l., 2007) a et Albin Department of Animal Science ( and breed breed and transport function differentially differentially function LPS breed and 4 h of 4 h of and breed to interaction; P < 0.05). < P interaction; by pig intestinal intestinal dependent glucose dependent - Na affected affected mall Ileal as LPS ( exposure responded responded S

82 1 - Sampling Euthanasia - d 4 - balance 4 injections) challenge N - challenge - d 3 Post Department of Animal Science (initial dose of 30 µg.kg Injection LPS with every 48h (total of - - ISS+ 6 ISS 8 ISS+ 6 ISS 8 every 48h (LPS) (LPS) - d 4 stimulation (ISS+) challenge N - balance Pre - challenge design Repeated intramuscular injection of increasingof Repeated intramuscularamounts injection of lipopolysaccharide increase BW, by 20, 30 and 40 % ) Control saline Immune system Immune system   - d adaptation 5 • General  n=14 HRFI LRFI n=14 Swine repeated Swine repeated LPS challenge model (Rakhshandeh et al.,)

83 0.001 0.52 0.01 0.001 0.60 0.01 < P values P < P values P ISS ISS × × ISS Line ISS Line Effect Effect Line Line Department of Animal Science LRFI LRFI 0.06 ± ± 0.06 ISS+ 0.6 0.6 ISS- Febrile Response Febrile – HRFI HRFI 0.06 0.07 ± ± 0.9 0.8 Eye temperature temperature Rectal 40.5 40.0 39.5 39.0 38.5 39.0 38.5 38.0 37.5 37.0 Results Results ºC ºC

84 ISS- ISS+ < 0.01 < < 0.01 < 0.01 P values P ISS LRFI × ISS Line Effect Line 3 Department of Animal Science HRFI LRFI ISS+ LRFI

60 40 20 80

PD g.d PD

1 - LRFI ISS- LRFI ISS- ISS+ 2 Period LRFI 0.30 0.33 0.05 HRFI ISS+ P values P ISS × ISS Line Effect Line HRFI HRFI ISS- 1

800 700 600 500 400 300 LPS and pig performance LPS and

68 66 64 62 60 Final BW, kg BW, Final

DM basis DM d . g

1 - Feed intake

85 ISS × 0.23 0.65 0.90 0.02 0.06 Line ISS P value P 0.17 0.03 0.07 0.04 0.21 0.02 0.38 0.70 0.01 0.01 Line Department of Animal Science SE 0.24 3.9 5.2 0.6 0.2 87 76 79 79 90 ISS+ LRFI and total tractdigestibility - 88 81 87 82 91 ISS ileal SE 0.65 6.3 0.3 1.1 1.1 86 80 90 79 78 HRFI ISS+ - 87 89 90 82 80 ISS D igestibility and % ISS with LPS reduces Crude protein Energy Crude protein Organic matter Organic matter LPS • AID, % ATTD,

86 0.78 0.55 0.38 0.28 0.05 0.015 P values P P values P ISS ISS Department of Animal Science × × ISS ISS Line Line Effect Effect Line Line LRFI LRFI ISS+ ISS+ ISS- HRFI ISS-

HRFI 1.2 1.0 0.8 0.6 protein) 0.4 0.2 0.0

2.5 2.0 1.5 1.0 0.5 0.0 relative to control) to relative ( U/mg U/mg ( expression

abundance abundance 2 Protein Protein 2 - MUCIN Ileum

2 gene gene 2 - MUCIN Ileum

87 ISS × 0.49 0.70 0.60 -ISS +ISS Line ISS P value P 0.05 0.56 0.60 P=0.63 P=0.11 P=0.12 LRFI 0.17 0.74 0.33 ISS Line Line Line x ISS x ISS Line SE Department of Animal Science 0.20 2.00 HRFI 29 data 5 4 3 2 1 0 0.6 12.0

ISS+

LRFI 139

- AU expression, mRNA Ileum GLUT2 Ileum 0.5 6.0 Blood ISS 135 SE and 0.94 2.00 15 1.6 10.0 HRFI ISS+ 126 P=0.86 2 - LRFI P=0.71 P=0.015 1.0 7.0 ISS 110 A/cm Line ISS Δµ Line x ISS Transport HRFI T 2 cm 3 2 1 0

mRNA expression, AU expression, mRNA , transport SGLT1 Ileum TER , Nutrient Glucose Glutamine Nutrient Ileum

88 40% RH. - 40% e70215. ONE 8(8): PLoS C for 24 h, 25 0.01 0.01 0.02 - value ° <0.01 P Department of Animal Science 1.08 3.55 18.6 8.3 ± ± ± ± Pearce et al., 2013al., Pearce et HS 88 7.9 102 15.7 0.93 3.55 17.4 7.2 ~50 kg pigs, constant constant pigs, 35 ~50 kg Environment ± ± ± ± µg/mL/min/cm TN 3.6 2.7 TN HS 182 133 2 2 b resistance permeability, 2 2 cm cm kDa x 4 x a . electrical 4 Ω Ω , , permeability permeability 1 1 4 4 FD TER FD TER Dextran - 0 50

200 150 100

Transepithelial FITC Parameter Ileum 1 2 AU Endotoxin, Ileum Colon Colon Heat stress reduces intestinal integrity and and integrity intestinal reduces stress Heat intestinal permeability endotoxin increases

89 Mycotoxins Department of Animal Science

90 10 :6 Department of Animal Science Nutrition & Metabolism Nutrition & Metabolism et. Al., 2013 et. Al., 2013 Mani Mani Dietary oil composition differentially modulates modulates differentially composition oil Dietary and transport postprandial endotoxin intestinal endotoxemia

91 10 :6 Department of Animal Science Nutrition & Metabolism Nutrition & Metabolism ., 2013 ., 2013 al et. Mani Mani Dietary oil composition differentially modulates modulates differentially composition oil Dietary and transport postprandial endotoxin intestinal endotoxemia

92 Department of Animal Science J2 treated J2 treated - 8 secretion IPEC 8 secretion from - Whole and lysed bacteria are potentandbacteriaWhole lysedimmunogens epithelial cells to pig intestinal IL with different LPS molecules apically apically with different with different LPS molecules apically •

93 Department of Animal Science - GFP F18 – host K12 or  model using the Chambers (ATCC) “Screening tool” Coli E. pmCherry Salmonella typhimurium Adherence Internalization Translocation • • • Ex vivo Ussing Labelled bacteria Bacterial 1. 2. 3. Bacterial translocation model translocation Bacterial • • •

94 . . Inh Inh 0.048 <0.001 P - value P - value D x D x oil TLR4/raft TLR4/raft 0.143 <0.001 value P - value value P - value Inhibitor Rhodobacter Inhibitor on ex vivo on ex vivo Diet Diet <0.001 P - value <0.001 Department of Animal Science P - value K12 flux. K12 raft inhibitorraft translocation in a SEM 105.8 SEM 139.1 E. coli b E. coli E. Fat b Fat 303 - raft 969 - TLR4 cyclodextrin High High diets composing of lard and soybean c c 4 specific - 4 specific antagonist (LPS from Fat methyl - methyl 1609 - Cont 1373 - Cont TLR High Fat High beta K12 flux. K12 a a 85 manner - raft 183 - TLR4 E. coli Low Fat Low Fat ) on ex vivo ) on ex jejunumvivo serosal on a ab Pigs consuming High Fat High Fat Pigs consuming have increased ex vivo intestinal mediated 187 188 - Cont - Cont Low Fat Low Fat • fu Dietary Dietary fat alters bacteria translocation content Effects of dietary fat and a Effects of dietary sphaeroides C jejunum serosal jejunum serosal Effects of dietary fat and Effects of dietary cfu

95 conditions Department of Animal Science verses Gram negative bacteria - absorptive effects (“leaky”) verses transcellular enter and post Dietary fat Probiotics/Prebiotics/fiber Phytogenic compounds Binders     Breed differences Pre - Enteric verses systemic pathogens Endotoxin/LPS Paracellular Inhibition of endotoxin/bacteria absorption across the gastrointestinaltract and attenuation of inflammation       Depending on the environmental and genetic Gram negativepathogenicbacteria/components antagonize livestock health and performance Conclusions  

96 Kerr Weber ARS, Ames, Ames, ARS, IA - Dr. Tom Tom Dr. Brian Dr. Department of Animal Science   USDA Iowa Pork Producers (IPPA) Association   , PhD (RFI) (Fatty acids) (Heat stress) Mani, PhD 20670 21778 30007 Rakshandeh 65206 - 67017 - 67003 - - Wes Schweer Curry Shelby PhD Pearce, Sarah Venkatesh Jim PhD Hollis, Joshua Lyte, PhD Anoosh EricDVM, Burrough, PhD • • • • • • • • 2010 - 2011 2014 - Iowa State University Acknowledgements # # This research This supported was by an Agriculture and Food Research Grants Competitive Initiative # •

97 . Biomedical Sciences Biomedical University Arizona of University Duarte Diaz Ph.D Diaz Duarte INTERACTIONS INTERACTIONS Department of Animal and Comparative Comparative and Animal Department of MYCOTOXIN EFFECTS: MAKING SENSE OF COMPLEX BIOLOGICAL

98 methods 2 increased? Better analytical • Why have mycotoxin concerns mycotoxin concerns Why have

99 Rapid monitoring Analysis - Methods Semi quantitative Analytical Analytical Mycotoxin Fully quantitative

100 4 g g 8.6 100 15.9 75.5 Ratio % Ratio Whitaker et al. 2005 Whitaker for about 75.5, for in in a lot of shelled Whitaker et al. al. 2005 et Whitaker errors account account errors 56.3 30.4 354.8 268.1 Variance by immunoassay immunoassay by associated with with a 0.91 kg sample,associated 50 respectively. , , and analysis and analysis the variance the variance . total error total preparation preparation 1 aliquot Mycotoxin Specific Mycotoxin aflatoxin Sampling Protocols , measuring aflatoxin in 1 aliquot measuring aflatoxin , ppb , 50g , , sample , sample 2 and of 8.6% the Total Immunoassay, Sample = 0.91 kg Sub S The variability The by variability measured subsample 20 at corn Sampling 15.9

101 53.3 137.5 The total The total a factor of five of five a factor by Increasing sample size sample size Increasing from five of factor a by cut will kg 4.54 to 0.91 the variance sampling in (80%). is reduced variance to 350.7 from (60%). from 266.3 to to 266.3 from Sampling Recommendations Whitaker et al. 2005 et Whitaker

102 0 0 0 0 0 10ppb 0 0 0 0 200 Concentration (USDA) 0 0 0 0 0 Aflatoxin Aflatoxin 0 0 0 0 0 Mean 9 13 12 12 14 12 11 12 14 12 (USDA) Protein 12 14 11 13 13 Protein Concentration 13% 13 13 15 13 12 Mold and Toxin Distribution Mold Mean

103 7 0.3 ppm 1.8 ppm 4.4 ppm 2.1 ppm – – – – DON ZONE ZONE DON ZONE ZONE 0.0 ppm 0.0 ppm – – DON ZONE ZONE Courtesy of Trilogy Labs Trilogy of Courtesy Visual Biases 0.0 ppm 7.0 ppm 82.8 ppm – – 263.2 ppm 263.2 – DON ZONE ZONE ZONE – ZONE DON

104 8 effects? increased? Better analytical Betteranalytical methods • Understanding Understanding of their occurrence and Why have mycotoxin concerns mycotoxin concerns Why have •

105 2001 Mycopathologia et. al al et. Bai 9 (ecological) Biological significance Biological Response to stress Response Competitive advantage advantage Competitive • Mechanisms for Mechanisms propagation • •

106 years 10 effects increased? Environmental stresses Environmental Better analytical Betteranalytical methods • Increased incidence some in Increased incidence • Understanding Understanding of their occurrence and • Why have mycotoxin concerns mycotoxin concerns Why have

107 A Tale of 5 years of 5 years A Tale

108 12 - 2010 - 7 - ppm +80% Samples +80% Samples 8 25 +60% Samples +60% Samples 1 ppm Courtesy of Trilogy Labs Trilogy of Courtesy DON Results in in 2009 DON Results Ohio/Indiana Corn and DDGS and Corn Ohio/Indiana

109 110 14 Map Report 2011 Harvest Mycotoxin

111 15

112 to t b; bje c to t b; b; c

bje e compiled from various sources and are su arevarioussources and from compiled e levels > 200 ppb; SC levels > 20 ppb; TX up to 1,000 ppb up to 1,000 levels 20 ppb; TX > levels 200 ppb; SC >

d levels as noted below. d levels as noted below. A

e C . For further product information visit information furtherproduct For . [email protected] n r ppm e ain sales staff. sales ain h r t 10

u < o . For further product information visit visit information product further For . PA 16 S : bols have confirm

N R 5333, or email us at us email or 5333,

O e compiled from various sources and are su are and sources various from compiled e levels > 200 ppb; SC levels > 20 ppb; TX up to 1,000 ppb 1,000 up to TX > 20 ppb; levels SC > 200 ppb; levels

COR d levels as noted below. below. noted as levels d C A e P 234- C [email protected] n r O ppm e ain sales staff. sales ain h R r t 10

u CROP CROP < o C (800) W PA W with colored sym with at

E d e us S NE N : bols have confirm have bols

k N r N N R a IN act I 5333, or email us at at us email or 5333,

O ng & Grain Department at Neogen. Reports ar Reports Neogen. at Department Grain & ng

N m COR N C I P 234-

a s r office and speak with our Milling & G & r office our Milling and speak with O S e I Milli R r CROP N C a (800) p W the O W with colored sym at

E o d r please cont e us M NE N by k r N s c IN act I t

ng & Grain Department at Neogen. Reportsar AFLATOXI FU

N m a

N s you I e n F a roffice and speak with ourMilling G & cont S s e I Milli r details OF e to O www.neogen.com r a p S the O N p o e r please L M by c r E s t FU AFLATOXI n V VELS a p you you further e F s E details brought OF e to O www.neogen.com r m LE t S p L is e L n For r D E e p r VELS V E a further r E brought brought Corn harvest report 10/01/12 u m LE M t L is n For D R C report e r : I IRMED r F u E M E R T C report report N IRMED I :

This F E our company website: website: company our variability. OK up to 200 ppb; KY > 20 ppb; AL up to 20 ppb; KS up to 80 ppb; MO up to 230 ppb ; IL up to 150 ppb; NE > 100 ppb, NC > 100 pp > NC 100 ppb, > up to 150 ppb; NE up to 230 ppb ; IL up to 80 ppb; MO up to 20 ppb; KS 20 ppb; AL > up to 200 ppb; KY OK TN >30ppb; IA up to 100 ppb; IN over 150 ppb up to 100 ppb; IN IA >30ppb; TN O O T N CONF

This our company website: website: company our variability. OK up to 200 ppb; KY > 20 ppb; AL up to 20 ppb; KS up to 80 ppb; MO up to 230 ppb ; IL up to 150 ppb; NE > 100 ppb, NC > 100 pp NC > 100 ppb, NE 150 ppb; up to IL 230 ppb ; up to MO 80 ppb; up to KS 20 ppb; up to AL > 20 ppb; KY 200 ppb; up to OK over 150 ppb IN 100 ppb; up to IA >30ppb; TN O O CONF ou contact please questions have you If N C

ou contact please If you have questions

C N 113 current jetstream Atlantic Atlantic . Programme 2016 - 2016 and Beyond 2015 truth is we don’t know what will happen. Will the two patterns reinforce each reinforce is we happen.don’t truth will what know Will patterns the two “The act other? in they each they going they to sequence?cancel Will other? Are Are of the Carlson, the director said David don’t know,” really We be regional? going to Research Climate World

114 – – 0.5 niña - La by a negative a negative by ONI greater than or equal to characterized characterized El niño characterized a positive by greater ONI than or equal +0.5 to

115 years 19 effects Agronomic practices Environmental stresses increased? Better analytical Betteranalytical methods • Increased incidence some in Increased incidence • Understanding Understanding of their occurrence and • Why have mycotoxin concerns mycotoxin concerns Why have

116 P ractices 20 Mansfield et. al. Plant Disease 2005 al. Plant et. Mansfield Impact of Agronomic

117 21 effects More general stress More general Genetic vulnerability Genetic increased? Better analytical Betteranalytical methods • Animal production production Animal changes/challenges Higher production (animals) production Higher levels Increased incidence years some in Increased incidence • • Understanding Understanding of their occurrence and • Why have mycotoxin concerns mycotoxin concerns Why have

118 10.9 2.24 21.9 2.61 3.81 2000 9.8 1.74 13.3 3.96 6.18 1960 Animal Production Changes Production Animal Overall Overall feed herd requirements Piglets / litter born Litter / sow/ year sow/ Pigs reared/ per year FCR (kg/kg)

119 2 2 - 23 acid (DON and T and (DON A produced produced acid Patuline PR toxin Penicillic Mycophenolic Aflatoxin Zearalenone Fumonisins Tricothecenes Penicillium Ochratoxin toxin) Well known Well know Least Major mycotoxins in Dairy mycotoxins Major

120 24 (Pestka and Casale 1989) Casale and (Pestka

121 Ergots ZEA FB1, DON Multi Multi Toxins Toxins M1 Mycotoxin Aflatoxin Aflatoxin - Multi Multi Toxins Multi Multi Toxins Toxins 2 - DON, T Heat Stress Heat Stress - Toxin DON New Area New Area Ergots ulti M ulti Toxins Toxins

122 strategies: - adsorption manufacturing practices Processing Processing and manufacturing Good Strict quality control - - - Decontamination - Physical Separation - Physical decontamination - Biological decontamination - Chemical inactivation - Chemo - residues crop - control Timelines Insect Management Management of Crop rotation Resistant Varieties Bio Drying Irrigation and nutrition mineral Cleanup ------Storage: Moisture and insect control Control Strategies harvest - harvest - Pre Management Harvest Management Post Management

123 control control / AF36 - Control Strategies Bio

124 . Mycotoxins Mycotoxins Department Department of Animal and Duarte Diaz Duarte Diaz Ph.D Comparative Biomedical Sciences Biomedical Comparative Industry: Feed Quality Industry: Feed Quality and Nutritional Toxicology Toxicology Nutritional Dairyin the

125 PhD Gott Paige Ruminant Technical Manager Technical Ruminant Occurrence of mycotoxins Occurrence of feeds dairy in California © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

127 , Klich (Bennett and methods by fungi on almost all agricultural commodities commodities all agricultural on almost mycotoxins identified identified mycotoxins chemically stable chemically and heat persistent during during storage persistent resistant to processing to processing resistant , secondary , secondary produced metabolites    roduced P worldwide 300 - 400 2003 ) High stability: High . . . oxic T . spp spp. spp. Aspergillus Penicillium Fusarium What are mycotoxins? © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

128 spp. Fusarium Fusarium spp. Myrothecium spp. Myrothecium Zearalenone Penicillium Penicillium spp. spp. Stachybotrys spp. Aspergillus Aspergillus Fusarium Fusarium spp. Acremonium Acremonium spp. A Fusarium Fusarium . Ochratoxin spp Trichoderma Trichoderma Fumonisins spp. . spp. Fusarium Fusarium spp Aspergillus Aspergillus Claviceps What are mycotoxins? alkaloids Aflatoxins Trichothecenes Ergot © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

129 spp. spp. Fusarium Fusarium Fusarium Fusarium spp. spp. Myrothecium spp. Myrothecium Myrothecium spp. Myrothecium Zearalenone Zearalenone Penicillium Penicillium Penicillium Penicillium spp. spp. spp. spp. Stachybotrys Stachybotrys spp. spp. Aspergillus Aspergillus Aspergillus Aspergillus Fusarium Fusarium Fusarium Fusarium spp. spp. Acremonium Acremonium Acremonium Acremonium spp. spp. A A Fusarium Fusarium Fusarium Fusarium . . Ochratoxin Ochratoxin spp spp Trichoderma Trichoderma Trichoderma Trichoderma Fumonisins Fumonisins spp. spp. . . spp. spp. Fusarium Fusarium Fusarium Fusarium spp spp Aspergillus Aspergillus Claviceps What are mycotoxins? alkaloids alkaloids Aflatoxins Trichothecenes Trichothecenes Ergot Ergot © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

130 fungi fungi damage depends on : depends Stress conditions of the plantsStress conditions Temperature Insect attacks Bird/wildlife Relative humidity . . . . . Mycotoxin production in thefield Production of mycotoxins by in the field Mitigation strategies: GMO strategies: GMO Mitigation pest corn, management strategies, Resistant plants, fungicide © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

131 Face management Packing density Mold inhibitors, inoculants . . . Moisture content Moisture Management Storage humidity/temperature/location Storage Storage . . . Storage fungi . © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

132 [ppb] 76 160 733 221 2326 5441 2043 1983 1570 5326 7764 1760 25928 99040 15620 13680 17659 12236 44616 Max 6.7 6.7 9.8 9.8 1.9 1.9 1.4 1.4 2.8 2.8 3.9 3.9 30.0 69.0 69.0 68.0 49.0 49.0 23.0 23.0 14.0 14.0 85.0 85.0 45.0 45.0 15.0 15.0 17.0 17.0 122.0 122.0 267.0 267.0 195.0 195.0 Median [ppb] Median [%] 98 96 89 71 63 89 66 65 65 87 87 84 82 80 80 76 75 63 63 ve + [n] 81 80 74 59 52 74 55 54 54 72 72 70 68 66 66 63 62 61 61 ve + Most Most out often detected of the 83 analyzed samples F acid methyl ether Enniatins Glucoside Mycotoxin/Metabolite - et al. 2013 al. et 3 - Hydroxyculmorin Streit Beauvericin Summe Deoxynivalenol Emodin Tryptophol Apicidin 15 - Tenuazonic Equisetin Zearalenone Aurofusarin Alternariol Brevianamide Alternariol Tentoxin Moniliformin DON Culmorin Nivalenol “Emerging” & “Masked” Mycotoxins © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

133 Murugesan Murugesan Raj e.g. DON mycotoxins Fungus produces produces Fungus ycotoxins m Austria. All rightsAustria. All reserved. infects plant Masked the © Copyright© 2014 by AG. Erber Fungus © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

134 Conjugation masked : by by = = ! ! plant glucoside the 3 - - of detected detected mycotoxin mycotoxin methods methods be be e.g. DON mechanism cannot cannot of sugarto the mycotoxin analytical analytical Defense mycotoxins mycotoxins mycotoxins mycotoxins conventional conventional ycotoxins Masked m Masked © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

135 is released cleaved in the gut: mycotoxin Increase in bioavailability Sugar  parental ycotoxins contaminated feed contaminated feed masked mycotoxins m ngests ngests containing Animal i Masked © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

136 exposure health status Duration of Duration Nutritional and Nutritional related factors: - related entities Other toxic temperature, etc) temperature, Farm management (hygiene, humidity, humidity, (hygiene, environmental and - animal - , species Age, sex Age, and mycotoxin toxin concentration Nature and level of … Mycotoxin effects © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

137 A Trichothecenes Ochratoxin Fumonisins effects alkaloids Ergot Mycotoxin Aflatoxins Zearalenone © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

138 1 - D3G D3GA DOM D15GA 3AcDON 15AcDON Abbreviations - influencing its toxicity: DON - - glucuronide epoxy glucuronide - - - metabolites acetyl DON acetyl 3 - acetyl - 15 - De - - - glucoside DON 3 15 O deoxynivalenol - DON 3 DON DON Fungi Plants Animals Bacteria Metabolized by Metabolized Deoxynivalenol derivatives DONbe canmodified fungi, animalsbacteriaby plant, and metabolites into several different © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

139 , graminearum towards DON DON towards up to 10 times than DON. than Fusarium Fusarium more D3G more but can contain contain can but producing producing More resistant resistant More Newly released Newly released wheat cultivars D3G DON to more convert efficiently . contamination in feed. Deoxynivalenol derivatives 3/15AcDON and 3/15AcDON D3G, can accountfor an of DON 75 % up to additional . © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

140 3400 ppb < 25 ppb DON ZEN 510 ppb 75 ppb DON ZEN 100 ppb < 25 ppb Mycotoxin detection DON ZEN © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

141 in feeds: Hot Spots! MYCOTOXINS MYCOTOXINS detection Uneven distribution of Mycotoxin Hot spots in silage: © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

142 Mycotoxin sampling Mycotoxin Reliability of measured levels of mycotoxins is greatly affected by the the by affected greatly is mycotoxins of levels measured of Reliability collection of samples representative © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

143 requirements consuming a wide a wide in More expensive More More time time More HPLC of Quantification single toxins at low concentrations + Fullfils legal - - matrices needed emerging & feed operator masked various of : method for qualified expensive MS/MS - mycotoxins mycotoxins Highly More More Suitable Detection LC ofdetection toxins multiple Simultaneous of commodities variety Sensitive + + + - - materials only Mycotoxin analytical methods Raw ELISA of Quantification specific mycotoxins matrices in given + Fast + Inexpensive - © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

145 Labs, Inc. Labs, Romer MS/MS at - 26 26 corn silage 23 almond hulls 26 cotton seed 25 corn 25 Occurrence of MTX in CA dairy feeds Occurrence of MTX in CA . . . . Samples collected from dairies in September 2016 September in dairies from collected Samples 100 samples total LC . . . © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

146 2 A , G 1 3 , G , B 2 2 , B , B MS/MS method 1 1 - B B . . Aflatoxins Fumonisins Zearalenone Ochratoxin . . . . panel X 17 analyzed mycotoxins via LC - DON - 2 - 2 Deoxynivalenol Neosolaniol Nivalenol Acetyl T HT Fusarenon Diacetoxyscirpenol BIOMIN mycotoxin mycotoxin BIOMIN ...... Type A trichothecenes A Type Type B trichothecenes Type . . © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

147 0 0 0 OTA 0 0 0 - A Trich 0 0 0 Afla 20 712 414 ZEN 80 635 1600 FUM 92 - 461 B 1054 Trich Parameters Occurrence of MTX in CA dairy feeds Occurrence of MTX in CA Average of positives [ppb] positives of Average contamination Maximum [ppb] % positive Summary corn of 25 analyses Summary © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

148 0 0 0 OTA 0 0 0 - A Trich 0 0 0 Afla 0 0 0 ZEN 167 300 23.1 FUM 0 0 0 - B Trich Parameters Occurrence of MTX in CA dairy feeds Occurrence of MTX in CA Summary corn analyses of 26 silage Summary % positive [ppb] positives of Average contamination Maximum [ppb] © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

149 6 6 4.3 OTA 0 0 0 - A Trich 0 0 0 Afla 0 0 0 ZEN 0 0 0 FUM 4.3 - B 1841 1841 Trich Parameters Occurrence of MTX in CA dairy feeds Occurrence of MTX in CA Summary almond analyses of 23 hulls Summary % positive % [ppb] positives of Average Maximum contamination contamination Maximum [ppb] © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

150 0 0 0 OTA 3.8 - 477 477 A Trich 0 0 0 Afla 7.7 128 176 ZEN 3.8 200 200 FUM 3.8 - 105 105 B Trich Parameters Occurrence of MTX in CA dairy feeds Occurrence of MTX in CA Summary seed analyses cotton of 26 Summary % positive % [ppb] positives of Average Maximum contamination contamination Maximum [ppb] © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

151 ) Trich - Type B Type (B Trichothecenes (OTA) (FUM) Fumonisins Ochratoxin A (ZEN) ) Zearalenone Trich - (Afla) Type A A Type (A Aflatoxins Trichothecenes 6 major mycotoxins Survey 3 labs 6 states US Corn MTX US Collected from from Collected 2 - MS/MS BIOMIN 2016 BIOMIN corn samples corn LC 387 © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

152 2% 98% A-Trich OTA 100% FUM 28% 72% Negative 2016 Corn US Crop – 75% 25% B-Trich Positive ZEN 42% 58% 6% Afla 94% 0 Sample Distribution 50

400 350 300 250 200 150 100 Number of samples of Number © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

153 6 42 75 72 2016 1 17 72 52 2015 - Deoxynivalenol X, Acetyl - X, Fusarenon 22 19 62 56 2014 8 2016 US Corn US Corn Crop 2016 14 27 54 2013 include Deoxynivalenol. Nivalenol, include Deoxynivalenol. – Trichothecenes and Ochratoxin are not presented due to small number of samplesof number smalldue to presented not are and Ochratoxin A - Trichothecenes B - Trichothecenes 30 22 33 72 2012 0 Prevalence Prevalence 80 60 40 20 © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

154 10 67 23 2016 15 39 46 2015 - Deoxynivalenol X, Acetyl - X, Fusarenon 37 13 50 2014 2016 US Corn US Corn Crop 2016 – 44 25 30 2013 include Deoxynivalenol. Nivalenol, include Deoxynivalenol. Trichothecenes and Ochratoxin are not presented due to small number of samplesof number smalldue to presented not are and Ochratoxin A - Trichothecenes B - Trichothecenes 35 54 11 2012 Occurrence - Occurrence 0 Co 80 60 40 20 © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

155 20 412 4424 2016 1670 39 247 2015 691 1190 - Deoxynivalenol X, Acetyl - X, Fusarenon 35 2014 15754 484 1424 2016 US Corn Corn US Crop 2016 – 51 2093 2013 542 include Deoxynivalenol. Nivalenol, include Deoxynivalenol. 143 Trichothecenes and Ochratoxin are not presented due to small number of samplesof number smalldue to presented not are and Ochratoxin A - Trichothecenes B - Trichothecenes 21 2012 138 2255 3351 Mean of Positives Mean of Positives 10 100 1000 10000 © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

156 management is management mycotoxin riskmycotoxin Summary Heterogeneous Heterogeneous distribution of mycotoxins of mycotoxins Variety essential for the protection of animals Proper in feeds Corn commonsource of mycotoxin contamination . . . . © 2017 Copyright by Erber AG. Austria. All rights reserved. © Copyright 2013 by Erber AG. Austria. All rights reserved. All rights Austria. AG. Erber by 2013 © Copyright

158 WINNER OF THE 2017 CANC ANIMAL NUTRITION SCHOLARSHIP

ASHLEY NIESEN

159 Livestock’s Contributions to Climate Change: Facts and Fiction A white paper, defining the role animal agriculture and other sectors of society play in their respective contribution of greenhouse gases, as the societal concerns grow to seek a sustainable global future.

Frank Mitloehner, Professor & Air Quality Specialist Department of Animal Science, University of California, Davis

As the November 2015 Global Climate Change Conference COP21 concluded in Paris, 196 countries reached agreement on the reduction of fossil fuel use and emissions in the production and consumption of energy, even to the extent of potentially phasing out fossil fuels out entirely. Both globally and in the U.S., energy production and use, as well as the transportation sectors, are the largest anthropogenic contributors of greenhouse gasses (GHG), which are believed to drive climate change. While there is scientific consensus regarding the relative importance of fossil fuel use, anti‐animal agriculture advocates, portray the idea that livestock is to blame for a lion share of the contributions to total GHG emissions.

One argument often made is U.S. livestock GHG emissions from cows, pigs, sheep and chickens are comparable to all transportation sectors from sources such as cars, trucks, planes, trains, etc. The argument suggests the solution of limiting meat consumption, starting with “Meatless Mondays,” which will show a significant impact on total emissions.

When divorcing political fiction from scientific facts around the quantification of GHG from all sectors of society, one finds a different picture. Leading scientists throughout the U.S., as well as the U.S. Environmental Protection Agency (EPA1) have quantified the impacts of livestock production in the U.S., which accounts for 4.2%2 of all GHG emissions, very far from the 18% to 51% range that advocates often cite. Comparing the 4.2% GHG contribution from livestock to the 27% from the transportation sector, or 31% from the energy sector in the U.S. brings all contributions to GHG into perspective. Rightfully so, the attention at COP21 was focused on the combined sectors consuming fossil fuels, as they contribute more than half of all GHG in the U.S.

1 http://www3.epa.gov/climatechange/ghgemissions/sources/agriculture.html 2 http://www3.epa.gov/climatechange/Downloads/ghgemissions/US‐GHG‐Inventory‐2015‐Main‐Text.pdf

160 Breaking down the 4.2% EPA figure for livestock by animal species, shows the following contributors: beef cattle 2.2%, dairy cattle 1.37%, swine 0.47%, poultry 0.08%, sheep 0.03%, goats 0.01% and other (horses, etc.) 0.04%. It is sometimes difficult to put these percentages in perspective, however; if all U.S. Americans practiced Meatless Mondays, we would reduce the U.S. national GHG emissions by 0.6%. A beefless Monday per week would cut total emissions by 0.3% annually. One certainly cannot neglect emissions from the livestock sector but to compare them to the main emission sources would put us on a wrong path to solutions, namely to significantly reduce our anthropogenic carbon footprint to reduce climate change.

= 2x

U.S. Population Replace Incadescent U.S. Population “Meatless Monday” with Energy Star bulbs – 1.2% = GHG Emission – 0.6%

In spite of the relatively low contributions to total GHG emissions, the U.S. livestock sector has shown considerable progress during the last six plus decades, and commitment into the future, to continually reduce its environmental footprint, while providing food security at home and abroad. These environmental advances have been the result of continued research and advances in animal genetics, precision nutrition, as well as animal care and health.

U.S. Dairy & Beef Production Continuous Improvement

1950 2015

Total Dairy Cows: 22 million dairy cows 9 million dairy cows (‐59%)

Milk Production: 117 million tons 209 million tons (+79%)

Carbon Footprint: 1/3 that of 1950

1970 2015

Total Beef Cattle: 140 million head 90 million head (‐36%)

Beef Production 24 million tons 24 million tons

Globally, the U.S. livestock sector is the country with the relatively lowest carbon footprint per unit of livestock product produced (i.e. meat, milk, or egg). The reason for this achievement largely lies in the production efficiencies of these commodities, whereby fewer animals are needed to produce a given quantity of animal protein food, as the following milk production example demonstrates: the average dairy cow in the U.S. produces 22,248 lbs. milk/cow/year. In comparison, the average dairy cow in Mexico produces 10,500 lbs. milk/cow/year, thus it requires 2‐plus cows in

161 Mexico to produce the same amount of milk as one cow in the U.S. India’s average milk production per cow is 2,500 lbs. milk/cow/year, increasing the methane and manure production by a factor of 9 times compared to the U.S. cow. As a result, the GHG production for that same amount of milk is much lower for the U.S. versus the Mexican or Indian cow. Production efficiency is a critical factor in sustainable animal protein production and it varies drastically by region.

Improvements in livestock production efficiencies are directly related to reductions of the environmental impact. Production efficiencies and GHG emissions are inversely related—when the one rises, the other falls.

The 2050 challenge to feeding the globe is real: throughout our lifetime, the global human population will have tripled from three to more than nine billion people without concurrent increases of natural resources to produce more food. Our natural resources of land, water and minerals (fertilizer) necessary for agricultural production, have not grown but in fact decreased. As a result, agriculture will have to become much more efficient worldwide and engage in an efficient path similar to the one it has traveled down in U.S. livestock production in recent decades.

How can emissions accurately and fairly be assessed to lay ground for a path for solutions?

In its quest to identify a sustainable, scientific path toward fulfilling the future global food demand, the Food and Agriculture Organization of the United Nations (FAO) has formed an international partnership project to develop and adopt a “gold standard” life cycle assessment (LCA) methodology for each livestock specie and the feed sector. The ‘Livestock Environmental Assessment and Performance Partnership’ (LEAP), engaged with more than 300 scientists from the world’s most prestigious academic institutions in developing this unprecedented effort in developing a global benchmarking methodology. The first three‐year phase project was finalized in December 2015 with six publically available LCA guidelines3. This globally harmonized quantification methodology will not only allow the accurate measurement by livestock species and production regions across the globe today, but will also identify opportunities for improvement and the ability to measure that progress in each region going forward.

3 http://www.fao.org/partnerships/leap/en/

162

Summary

Addressing the 2050 challenge of supplying food to a drastically growing human population can sustainably be achieved through intensification of livestock production. Indeed, intensification provides large opportunities for climate change mitigation and can reduce associated land use changes such as deforestation. Production efficiencies reduce environmental pollution per unit of product.

The U.S. livestock, poultry and feed industries are one of the most efficient and lowest environmental impact systems in the world. The research, technologies and best practices that have been developed and implemented over time in the U.S. can also be shared with other production regions around the world. It is important to understand that all regions have unique demands and abilities, and thus require regional solutions. However, the advances in the U.S. agriculture and food system can be adapted within these regional solutions. These significant environmental advances and benefits are in addition to the well‐documented human health and developmental value of incorporating animal protein in the diets of the growing population.

The livestock sector is committed to continuous improvement of their environmental impact in North America, and to doing its part in transferring knowledge, technologies and best practices to enhance global environmental livestock impact by region. Now is the time to end the rhetoric and separate facts from fiction around the numerous sectors that contribute emissions and to identify solutions for the global food supply that allow us to reduce our impact on the planet and its resources.

163 The California ARPAS Alfalfa Hay Project California Chapter of ARPAS

Introduction

Analyses of feeds that do not allow for prediction of animal performance from inputs, and vice versa, lack utility. The first report of a system developed to predict input from output was in 1809 (Von Thaer, cited by Flatt et al., 1969); Von Thaer and associates compared body weight change of steers fed different feeds to a standard (“good meadow hay”). A simpler yet demonstrably less accurate scheme based on solubility of feeds in various dilute solutions was than employed, which led to another system and another system and so on. The history of feed analysis is replete with simple yet inaccurate feeding systems.

Energy based systems evaluate feeds and animal performance based on the conservation of energy and were developed in Europe; early feeding standards such as starch and barley equivalents, continued in use well past the middle of the last century. In the United States, however, the TDN system persisted until the second half of the last century. It is against this backdrop that Jim Meyer and Glen Lofgreen conducted studies to “establish regression equations which would be useful in predicting the total digestible nutrient (TDN) content from lignin or crude fiber analysis” in alfalfa hay (Meyer and Lofgreen, 1956). Correlation coefficients (R2) for estimating TDN from either lignin or crude fiber were 0.77 and 0.74, respectively. Meyer and Lofgreen (1959) stated that the previous study was useful only in controlled nutritional investigations and had little practical application in the field. A subsequent study was undertaken to “develop a procedure by which chemical analyses could be used to reduce much of the uncertainty when hay is graded or evaluated for livestock feed”. Included in the experimental work were:

Choice and simplification of analytical methods Development of new regression equations Development of tables so that monetary value of hay could be readily calculated

A modification of the crude fiber procedure was found to be superior in predicting TDN (R2 = 0.79) when compared with predictions based on either lignin or crude fiber. These investigators also demonstrated that the relationship between NE/TDN and crude fiber was curvilinear. This observation is critical in evaluating predictive accuracy and model specification of current prediction equations, which are generally linear. Meyer and Lofgreen (1959) further suggested pricing energy and protein in alfalfa relative to other feeds, such as barley and cottonseed meal; a table was included in that paper. Since that time alfalfa hay has been traded in California on the basis of TDN content.

With the advent of detergent fiber analyses (Goering and Van Soest, 1970) studies were undertaken at UC Davis as part of a western regional project investigating the relationship between TDN and acid detergent fiber content of alfalfa hay. Our analysis of data from alfalfa digestion studies, using wether lambs (W.N. Garrett, unpublished data) indicated that the correlation between TDN and ADF was less (R2 = 0.72)than that reported by Meyer and Lofgreen (1959) for modified crude fiber. Garrett also stated, and our analysis of those data bear this out, that digestible energy was more highly correlated (R2 = 0.80) with ADF than was TDN. However, regardless of predictor (ADF, crude fiber or modified crude fiber) or response variable (TDN or DE) the response vector failed to mimic the observed vector (P < 0.05). Further analysis of these data indicated that estimating equations of the form:

164

TDN = a + b x fibrous entity (ADF, crude fiber or modified crude fiber) when determined for lambs and steers were misspecified (P < 0.05). Misspecification of a linear model suggests the relationship between response and predictor variable is non-linear and other forms of the model should be investigated.

Consumption of ME provides fuel for processes associated with service and repair functions (maintenance), such as maintenance of ionic gradients, lipid and protein turnover or as Schiemann (1969) noted “The maintenance requirement is a requirement for ATP-equivalents”. Metabolizable energy is also a property of substrates for synthesis of biomass, such meat, milk, fiber and products of conception (Baldwin, 1995 and Kennedy and Calvert, 2014).

Given the anachronism that is the TDN system and the lack of global fit to any of the current TDN estimating equations, the California chapter of ARPAS undertook this study to determine if ME content of pure stand alfalfa hay could be predicted from the NIR spectrum.

Materials and Methods

During the 2008 growing season pure stands of alfalfa hay were sampled throughout California and western Nevada. Sampling was done on roadsided stacks and barn- stored alfalfa; we sampled more than 200 lots of hay. Nine of the samples were chosen, based on analytical (Table 1) and environmental diversity (Table 2), for use in a lamb metabolism study conducted at the USDA Dairy Forage Research Center, Madison, WI. Lambs were fed hay cubes that had been reground and pelleted; each hay sample (n = 9) was fed to six lambs at two intakes, either maintenance (~2 percent of body weight) or ad libitum (~5 percent of body weight). Lambs were adapted for seven days followed by a seven day collection of urine and feces; intake energy, fecal energy and urinary energy were observed while gaseous energy was estimated from in vitro carbohydrate degradation.

There exist a number of prediction equations for estimating quality of alfalfa hay. We evaluated the current UC TDN equation (Putnam et al., 2007), relative feed value (Rohweder et al., 1978), relative feed quality (Moore and Undersander, 2002) and the NRC (2001) summative equation for model specification and internal validity using data developed from this study. In addition, a least squares estimating equation (OLS) of the form TDN = f(ADF) was developed for data from this study to further evaluate model structure and utility. Parametric stability and concordance of NDF degradability determined either in vivo, in vitro or by Bayesian inference (uninformed priors) for the NRC (2001) TDN equation were evaluated.

Rate and extent of in vitro degradation (120 hours) was evaluated using either a stochastic, heterogeneous rate model:

-α Rt = D x (1 + β (t-τ) ) + I

where:

Rt = analyte residue at time t (h), R0 = 1.00 (arbitrary unit) α, β = shape and scale parameters of gamma distributions t = time after inoculation of medium (h) D = potentially degradable fraction τ = time delay before losses begin or lag (h)

165 I = analyte incapable of being degraded or a first order function:

k (t-τ) Rt = D x e + I where:

k = rate constant (h-1)

Metabolizable energy and TDN were estimated from the alfalfa hay NIR spectrum. For spectral analysis, all alfalfa hay samples were oven dried and ground before placement in a spectrophotometer. Reflectance values for λ were from 950 to 2,500 nm and were analyzed using the chemometric utility UNSCRAMBLER (CAMO Software, Norway) to develop NIR prediction models for ME or TDN at either restricted or ad libitum intake.

Results and Discussion

California estimating equation

The California TDN estimating equation: TDN = 82.38 – 0.7515 x ADF failed to predict TDN observed in this study (Lin’s correlation coefficient (ρc) = 0.793 and root mean square error (RMSE) = 2.58. Lin’s correlation coefficient measure the extent to which two vectors mimic each other; ρc = 1.0 indicates a perfect correspondence and ρc = 0 indicates that no relationship exists. The equation estimated for this study: TDN = 79.3 – 0.699 x ADF also failed to predict observed 2 TDN (R = 0.773,ρc = 0.856, RMSE = 1.99). Parametric instability was noted for the slope of the previous regression; a 95% confidence interval about the slope was from 0.594 to 0.804. Ordinary least squares regression, for both estimating equations, of vectors TDNpredicted and TDNobserved indicated that the vectors were different; the slope was not equal to one (P < 0.05) and the intercept was not equal to zero (P < 0.05). Figure 1 shows that, for TDN observed in this study, predicted TDN (UC equation) overestimates observed TDN; also shown is the prediction interval which averages ± 5.35 points of TDN (8.82%). A significant F ratio was noted for model misspecification; misspecification of a linear model indicates that the relationship between response (TDNobserved) and predictor variable(s) (ADF in this case) is non-linear and that other models and predictor variables should be evaluated. Residual heteroscedasticity (Figure 2) is further indication of inappropriate model specification. The uncertainty in alfalfa evaluation, of which Meyer and Lofgreen (1959) wrote, does not appear to have been resolved by the current estimating equation.

RFV and RFQ

Relative feed value (RFV) assumes that NDF is predictive of ad libitum dry matter intake (DMI) and that ADF is predictive of dry matter digestibility (Rohweder et al., 1978). Dry matter intake (ad libitum) was poorly predicted by NDF content of alfalfa hay. For the ordinary least squares relationship DMIcalculated = f(DMIobserved) the slope was not different from zero clearly indicating that, contrary to the assumption of Rohweder at al. (1976), no relationship exists between NDF content of alfalfa hay and dry matter intake. Lin’s correlation coefficient (ρc = -0.159) also indicated that the vector DMIcalculated did not mimic the vector DMIobserved. Studies by Sanson and Kercher (1996) and Hackman et al. (2008) indicated no relationship existed between dry matter intake and NDF content of alfalfa hay. Van Soest et al. (1978) noted “the common assumption that digestibility and intake are always

166 positively correlated is unsafe…” which is consistent with with our results and those of Sanson and Kercher (1996) as well as Hackman et al. (2008). Dry matter digestibility (DDM) was poorly predicted by ADF content; for the equation DDMcalculated = f(DDMobserved) the slope was different from one and the intercept different from P zero ( < 0.05). Lin’s correlation coefficient (ρc = 0.402), while greater than that calculated for the relationship between vectors DMIcalculated and DMIobserved, ρc of 0.402 clearly indicates a lack of correlation, providing further evidence that dry matter digestibility was poorly predicted by ADF. Relative feed value, calculated as f(NDF & ADF) is not equal to observed RFV (P < 0.05) and Lin’s correlation coefficient = 0.0525. Utility in the calculation of RFV does not reside in the prediction of dry matter intake nor dry matter digestibility nor in the prediction of observed RFV. Metabolizable energy is correlated (R2 ~ 0.88) with both NDF and ADF; the use of either term should be preferred to RFV. Quality estimates, such as RFV, are often presented as fait accompli with little regard to predictive accuracy or model structure.

Relative feed quality (RFQ) differs from RFV in that TDN replaces ADF in the equation. According to Moore and Undersander (2002) RFQ is preferred because the estimating equation is improved. Really. When observed TDN is used in the RFQ P estimating equation (DMI estimated as f(NDF)) RFQcalculated ≠ RFQobserved ( < 0.05), ρc = 0.0500. Replacing observed TDN with TDN calculated from the UC TDN equation did not improve the fit; ρc = 0.0424. This analysis indicates that RFQ, when estimated as described by Moore and Undersander (2002), is as predictive of RFQobserved as estimates of RFV are of RFVobserved.

Summative estimates of TDN

The classical definition of TDN: (% digestible crude protein + % (digestible ether extract x 2.25) + % digestible crude fiber + % digestible nitrogen free extract) is a summative equation. The NRC (2001) publication “Nutrient Requirements of Dairy Cattle” suggests the following digestion coefficients: crude protein – 93%, ether extract – 97%, nitrogen free extract – 98% and an additive constant of -7; the additive constant is inconsistent with the classical definition of TDN. In vitro degradation of NDF (48 hours) replaces NDF degradation observed in a metabolism study. Total digestible nutrients, estimated using the NRC (2001) summative equation, failed to predict observed TDN; the slope of the OLS regression TDNpredicted P = f(TDNobserved) was different from one ( < 0.05) and the intercept was different from zero (P < 0.05). Further evidence of the lack of correspondence of estimated TDN with observed TDN is provided by Lin’s correlation coefficient (ρc = 0.760). Digestion coefficients found in the NRC (2001) summative equation are different (P < 0.001) than those observed in this study. In vitro degradation of NDF (48h) was different from observed in vivo (P < 0.001) These observations are consistent with the statement by Hungate (1966) “In vitro experiments as usually conducted do not provide reliable estimates of the rates at which the phenomena under study occur in the rumen”. Digestion coefficients for unpublished data from W.N. Garrett (late professor emeritus, The University of California, Davis) for cattle fed alfalfa at maintenance were different (P < 0.001) as well. The digestion coefficient for ether extract was 0.0059 (Garrett data) and a 95% confidence about that value included zero. It appears that the estimate of ether extract digestibility found in the NRC (2001) estimating equation may be different from the true parameter for alfalfa hay.

Parameter estimates (presumed digestion coefficients) for the OLS (zero intercept) regression TDN = f(CP, EE, NDF and NSC) for data from the California ARPAS study P were different from observed digestion coefficients ( < 0.05), however, ρc = 0.912 a value greater than that for other TDN estimating equations. Ninety five percent

167 confidence intervals about digestion coefficients for crude protein and ether extract included 1.00 indicating an infeasible solution. Increasing the number of predictor variables may improve predictive accuracy but the model is wrong. When an additive constant was included, only the parameter estimate for NDF was not different from zero (-0.603). Inappropriate sign and magnitude of parameter estimates (an additive constant different from zero and digestion coefficients less than zero) are clear indicators of a misspecified equation. This observation lends further credence to the observation that OLS equations poorly predict TDN for California alfalfa hay. Confidence intervals (95%) for standardized regression (means = 0, standard deviations = 1) coefficients for crude protein, ether extract and nonstructural carbohydrates were not different from zero while that for NDF was -0.704 and a 95% confidence interval was (-1.24 to -0.172). The additive constant for such equations is necessarily zero and lack of stability in all parameter estimates provides further evidence of model misspecification. The sign and magnitude of the coefficient for NDF does indicate variability in NDF is responsible for more variability in TDN than the other predictor variables (CP, EE and NSC).

Comparisons of NDF degradability observed in vivo, in vitro and estimated using the NRC (2001) TDN equation (Bayesian inference) were different (P < 0.05) with the exception of the Siskiyou sample. Degradability of NDF, for the Siskiyou sample (0.357) in vivo (lambs fed ad libitum), was not different from the Bayesian estimate (95% credible interval = 0.338 to 0.358) of NDF degradability in the NRC (2001) summative equation. Degradability of NDF (in vitro) observed at either 30 or 48 hours was different from that estimated using Bayesian inference (P < 0.05) indicating that the NRC (2001) TDN equation may be inappropriate for use with pure stand alfalfa hay from California. The more complex summative equation (NRC, 2001) fails to predict TDN with the accuracy of a single variable OLS equation, the latter is preferred.

Near infrared estimates of TDN and MEI

Both TDN and ME, for lambs fed ad libitum, were well predicted from the NIR spectrum; R2 for TDN was 0.90 and the standard error of calibration (SEC) was 1.3. When TDN was estimated from ADF the RMSE (RMSE = SEC) was 1.99; error in the estimate was reduced. For ME predicted from the NIR spectrum R2 was 0.92 and the standard error of calibration was 0.07. Average estimated ME was 2.19 Mcal/kg and the range was from 1.82 Mcal/kg (Imperial #2) to 2.57 Mcal/kg (Stanislaus). Estimates of alfalfa quality, determined by NIR spectroscopy, are more accurate than those determined using the current system; further testing is required to make the system functional.

Acknowledgements

The California Chapter of ARPAS greatly appreciates the significant financial support of Forage Genetics International and Dow Pioneer. We would also like to thank Dave Mertens for supervising the metabolism study. Without the help of Don Sapienza in sampling, conducting the in vitro studies and performing the NIR analyses this project would not have been possible.

References

Baldwin, R.L. 1995. Modeling ruminant digestion and metabolism. Chapman and Hall. London, UK.

Blaxter, K.L.. 1969. The efficiency of energy transformation in ruminants. In: K.L. Blaxter, J. Kielanowski and Greta Thorbek (Eds.) Energy Metabolism of Farm Animals.

168 Pages 21-40 Eur. Assoc. Anim. Prod. Publ. No. 12. Oriel Press, LTD. Newcastle upon Tyne, UK.

Flatt, W.P., P.W. Moe, L.A. Moore and P.J. Van Soest. 1969. Estimation and prediction of the energy value of feeds for ruminants. In: K.L. Blaxter, J. Kielanowski and Greta Thorbek (Eds.) Energy Metabolism of Farm Animals. Pages 59- 65. Eur. Assoc. Anim. Prod. Publ. No. 122 Oriel Press, LTD. Newcastle upon Tyne, UK.

Goering, H.K. and P.J. Van Soest. 1970. Forage fiber analyses: Apparatus, reagents, procedures and some applications. Agric. Handbook 379. Washington, D.C.: United States Department of Agriculture.

Hungate, R.E.. 1966. The Rumen and its Microbes. Academic Press. San Francisco, CA

Kennedy, K.M. and C.C. Calvert. 2014. Effects of assumptions on estimating energetic efficiencies in lactating dairy cattle. J. Anim. Sci. 92 E. Suppl.2:870.

Mertens, D.R. 2002. Gravimetric determination of amylase-treated neutral detergent fiber in feeds with refluxing in beakers or crucibles: collaborative study. J. AOAC Int. 65:1217-1240

Meyer, J.H. and G.P. Lofgreen. 1956. The estimation of the total digestible nutrients in alfalfa from its lignin and crude fiber content. J. Anim. Sci. 15:543

Meyer, J.H., and G.P. Lofgreen. 1959, Evaluation of alfalfa hay by chemical analysis. J. Anim. Sci. 1233-1242.

Moore, J.E. and D.J. Undersander. 2002. Relative forage quality: an alternative to relative feed value and quality index. Proc. 13th Ann. Florida Ruminant Nutr. Symp. p 16-32.

NRC. 2001. Nutrient Requirements of Dairy Cattle. 8th. ed. Natl. Acad. Sci. Washington, D.C.

Putnam, D.H., P. Robinson and E. DePeters. 2007. Forage testing and quality. Chapter 16 In: C.G. Summers and D.H. Putnam, Eds. Irrigated Alfalfa Management in Mediterranean and Desert Zones. University of California Agriculture and Natural Resources Publication 8302. Oakland, CA.

Rohweder, D.A., R.F. Barnes and Neal Jorgensen, 1978. Proposed hay grading standards based on laboratory analyses for evaluating quality. J. Anim. Sci. 47:747-759.

Sanson, D.W., and C.J. Kercher. 1996. Validation of equations used to estimate relative feed value of alfalfa hay. Prof. Anim. Sci. 12:162-166.

Schiemann, R. 1969. The scientific demands made of a system for evaluating feeds as energy sources and progress made towards their realization. In: K.L. Blaxter, J. Kielanowski and Greta Thorbek (Eds.) Energy Metabolism of Farm Animals. Pages 31- 40 Eur. Assoc. Anim. Prod. Publ. No. 12. Oriel Press, LTD. Newcastle upon Tyne, UK.

Van Soest, P.J., D.R. Mertens and B. Deinum. 1978. Preharvest factors influencing quality of conserved forage. J. Anim. Sci. 47:712-720.

169

2 E (Mcal/kg) I 4.40±0.009 4.31±0.007 4.33±0.011 4.34±0.005 4.26±0.008 4.44±0.011 4.29±0.005 4.25±0.007 4.22±0.004

2 CP (%) 20.3±0.036 18.0±0.118 20.3±0.173 24.2±0.154 20.1±0.203 31.8±0.124 19.7±0.154 25.1±0.132 20.6±0.153

2 6.10±0.108 6.32±0.105 4.90±0.064 4.56±0.058 5.38±0.097 3.47±0.055 5.48±0.049 3.39±0.020 4.88±0.063 Lignin (%)

2 %) ADF ( 33.0±0.342 34.9±0.163 23.5±0.104 24.6±0.185 29.3±0.318 19.1±0.185 29.2±0.177 20.2±0.127 26.9±0.153

2,3 aNDF (%) 40.1±0.199 41.9±0.222 31.4±0. 273 30.7±0.187 37.0±0.186 24. 7±0.229 35.5±0.198 25.6±0.182 33.8±0.216

2 OM (%) 90.6±0.032 89.2±0.064 89.2±0.043 89.1±0.066 89.2±0.034 91.1±0.043 89.4±0.052 87.3±0.042 88 .1±0.086

2 DM (%) 87.4±0.179 87.7±0.250 88.9±0.227 90.0±0.183 87.6±0.192 87.8±0.205 86.7±0.187 87.1±0.099 85.7±0.283

Means are shown ± SEM. Amylase treated NDF (Mertens, 2002) California county of origin; Mason Valley is in Lyon County, Nevada.

Source Table 1. Composition of samples Imperial #1 Imperial #2 1 2 3 Kern #1 Kern #2 Merced Stanislaus San Joaquin Siskiyou Mason Valley

170

78 78 96 65 94 70 95 106 106 Maximum temperature, °F

80 80 44 44 63 44 59 23 53 Minimum temperature, °F

May July July July August September May (early) May (early) March (mid) Month cut

1,2

California county of origin; Mason Valley is in Lyon County, Nevada. All cuttings were in 2008. Kern #1 Source Table 2. Cutting times, average temperature minima an d maxima Imperial #1 Imperial #2 1 2 Kern #2 Merced Stanislaus San Joaquin Siskiyou Mason Valley

171 0.7515 x ADF Figure 1. Relationship between observed TDN and TDN predicted as 82.38 –

172

173 TDN predicted as calculated by the current UC estimating 0.7515 x ADF equation; TDN = 82.38 – Figure 2. Plot of residuals for TDN observed -

174

175 176 What fiber digestibility fiber digestibility What analyses do you use? mean do they …what cow? the to

177 The list con2nues… con2nues… list The as marker 240 or uNDF or 120 12, 24, 30, 48, 72, 120, 240 h uNDF produc2on biomass microbial and rates CHO diges2on ! ! ! Commercial lab assays for digestibility… NDF Lignin, lignin/NDF or ADF situ in vitro, in uNDF ) NDF diges2bility (or Apparent total tract diges2on of NDF, TMR-D TTNDFD Fermentrics – gas produc2on systems ! ! ! ! !

178 Lignin = “plant plastic” plastic” Lignin = “plant Digestion in Digestion rumen the Range: 11-20% Goal: <15% Range: 3-9% Goal: <6% Goal: <9% ! ! ! ! ! Alfalfa silage Corn silage Grass Can we simply use lignin ratios? or L/NDF ! ! !

179 ? ? ? ? ? 30-h NDFD 30-h NDFD 3.26 3.32 3.18 3.43 3.52 Lignin, % Lignin, 45.0 45.0 45.1 45.0 45.0 Corn silage data set from Van Amburgh (2005) set Corn silage data NDF 51.8% to from 36.5 Similar relationships " " NDF, % NDF, Measured NDFD or Measured NDFD from Lignin? Estimation

180 67.3 55.0 54.4 48.4 46.0 30-h NDFD 30-h NDFD 3.43 3.18 3.32 3.26 3.52 Lignin, % Lignin, 45.0 45.1 45.0 45.0 45.0 Corn silage data set from Van Amburgh (2005) set Corn silage data NDF 51.8% to from 36.5 Similar relationships " " NDF, % NDF, Measured NDFD or Measured NDFD from Lignin? Estimation

181 Future for lignin and ADF? Future

182 batch in vitro system batch in vitro system Tilley-Terry “artificial rumen” Tilley-Terry : why is it important? important? : why is it uNDF

183 (30 h) = 15.5% (120 h) = 13.1% (240 h) = 8.8% Lignin*2.4 = 3.67%/h uNDF240 = 5.91%/h

# # uNDF uNDF NDF digestion rate Lignin = 2.65% of DM aNDF = 36.9% of DM uNDF

# # # # # #

184 ? and NDF rate of Forage quality assessment Benchmarking: (+) feedback from field More accurate rates = more milk predictions. digestion. environment, genetics, and maturity than L/NDF or ADL x 2.4. NDF to calculate pdNDF uNDF240 is more sensitive to growing Need accurate measure of indigestible $ $ $ Why the focus on uNDF Why the 1. 2.

185 ?

in TMR in feces , fast & slow NDF uNDF ~ 1% decrease in DDMI with analy2cal value analy2cal with 0.67 uNDF ADL/NDF in rumen is 1.6x in TMR equals uNDF 0, 30, 120, and 240 h for forages 0, 12, 72, and 120 h for NFFS Best benchmark: 30 or 240 hour? uNDF Replace uNDF 1% increase in DMI prediction Biological reality: uNDF Forage energy value – NRC equation $ $ $ $ $ $ $ Why the focus on uNDF Why the 4) 3) 7)

186

240 when formulating diets diets when formulating that we need to capture capture we need to that and predicting cow response! cow response! and predicting Tremendous variation in uNDF Tremendous variation 8.7% of DM DM of 8.7% 25.5% to 2.0 Range: DM of 17.6% 31.7% to 5.5 Range: DM of 15.5% 44.8% to 2.3 Range: ------Corn silage Legume silage Grass silage % % % Measured ranges in uNDF newsletter) 2015 May One, Dairy (source:

187 ) Flis (Dairy One, April 2016 Newsletter, S. S. Newsletter, 2016 April One, (Dairy 240 Differing genetics, maturity, and environment – Differing genetics, maturity, Differing crosslinking between lignin and CHO Variation in lignin and Variation uNDF

188

Biological reality and Biological reality of uNDF kinetics

189 uNDF240 Slow Pool Fast Pool is needed to measure is needed to measured at 240 h of in vitro fermenta2on fermenta2on vitro in of at 240 h uNDF measured pdNDF = NDF - uNDF pdNDF has 2 frac2ons: fast-NDF and slow-NDF " " " uNDF and slow NDF fast

190 Slide courtesy of Dr. Mike Allen Mike Dr. of courtesy Slide (Allen, 2005, unpublished) 2005, (Allen, Slow Slow Fast Fast Fast and slow NDF exists in exists and slow NDF Fast all forage types

191 192 250 : 200

150 Faster ea2ng disappearance Faster ruminal Higher intakes Larger fast pool $ $ $ Time, h h Time, 100 =11%/h 1 50 k P1 = 72% of NDF Corn Silage Example: Example: Silage Corn 3-Pool NDF Diges2on 0

1.000 0.800 0.600 0.400 0.200 0.000 NDF residue residue NDF

193 : 250 pools uNDF 200 = 0%/h

uNDF uNDF = 9.9% of NDF k 150 More “ballast” chewing Greater Slower ea2ng speed Lower intake Time, h h Time, Larger slow and $ $ $ $ 100 = 2%/h 2 50 k P2 = 18.1% of NDF Corn Silage Example: Example: Silage Corn 3-Pool NDF Diges2on 0

1.000 0.800 0.600 0.400 0.200 0.000 NDF residue residue NDF

194 195

NDFom NDFom uNDF30om uNDF240om Low Low lb /d lb /d lb /d 5-7 0.40% BW 0.40% 12-14 21-22 High Pen Pen Fresh Fresh Close-up Close-up Far-oﬀ Far-oﬀ Miner Herd Dietary NDF and NDF Miner Herd Dietary Targets Intake: uNDFom

5 0

25 20 15 10

/d /d lb Intake,

196 Grass Forage Forage Grass Mixed Forage Forage Mixed uNDF240om uNDF240om Legume Forage Legume Forage uNDF120om uNDF120om High NDFD High NDFD Corn Silage Corn Silage (CVAS 2015-2016) uNDF30om uNDF30om Corn Silage Corn Silage profile: 30, 120, and 120, profile: 30, uNDFom 240 h

5 0

40 35 30 25 20 15 10 %DM %DM

197 uNDF240om uNDF240om uNDF120om uNDF120om (CVAS 2015-2016) uNDF30om uNDF30om

0 5

30 20 10 35 25 15 profile: 30, 120, and 120, profile: 30, uNDFom 240 h %DM %DM

198 199 36-55% corn silage 39 to 68% total forage NDFD) (±10%-units Conven2onal vs BMR to 10%) Added straw (up High forage vs NFFS diets ~61 lb /d DMI ~100 lb /d SCM ! ! ! ! ! ! ! Substan2al range in diet forage base Cows responded predictably to NDF, NDFD High-performance cows across the studies Perspectives from Miner Perspectives Studies… ! ! !

200 is approximately uNDF is in feces uNDF in par2cles from HCS and CS (30-40 hours) hours) CS (30-40 HCS and from par2cles (0.63/24) Equates to rumen passage rate of 2.6%/h for uNDF (0.63/24) Agrees with recent measures of rumen MRT for marked NDF ! ! Benchmarks from Miner Benchmarks Theory? Studies…String intake is ~1.47% of BW Maximum aNDFom intake is ~1.47% of BW BW) of Maximum rumen aNDFom is 8.5 kg (1.28% Range in uNDF intake is 0.30 - 0.45% of BW Range in uNDF mass in rumen is 0.48 - 0.62% of BW equals diet uNDF in Ra2o of intake uNDF /rumen diet… of 0.63 regardless ! ! ! ! ! !

201 Range in TMR uNDF240 Range in TMR uNDF240 (% of BW)

202 , Fast and Slow NDF and Slow NDF Fast , Rumen Fill Dynamics: uNDF

203 High quality forage in the High quality future…

204 205 35.1 60.3 33.8 High BMR 64% forage 51%BMR:13%HCS 51%BMR:13%HCS 31.5 62.0 35.1

Low BMR 49% forage 36%BMR:13%HCS 36%BMR:13%HCS 0.05). ≤

P 35.6 54.0 35.7 High CCS 67% forage 54%CS:13% HCS 32.1 56.3 34.7 Low CCS 53% forage 40%CS:13% HCS

Forage NDF and time Forage NDF eating… spent Least squares means within a row without a common superscript differ ( differ withoutsuperscript common a withinrow means a squares Least

Item TMR NDF, % of DM TMR 24-h NDFD, % Ea2ng Behavior % of TCT abc

206 2.6 3.5 2.2 1.7 1.9 0.4 0.7 0.6 Avg Chews Chews /g NDF /g NDF 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 SEM c a bc bc bc ab c d Bolus, mm Bolus, 9.9 8.1 LSM 10.3 12.5 10.7 10.8 11.2 11.6 … 2.7 1.3 0.2 0.2 0.3 0.3 0.2 SEM c e a a d b f … Feed, mm Feed, 9.7 LSM 13.8 12.0 42.2 43.5 13.1 25.1 57.1 58.6 57.9 59.1 54.2 53.1 48.1 37.7 %NDF Particle size of ingested feed size of ingested Particle 2011) al., et ( Schadt Long rye grass hay 50-mm rye “hay” 19-mm PSPS hay 8-mm PSPS hay PSPS hay 1.18 Grass silage Corn silage TMR

207 Comments Comments especially if >10% Maximize amount on this sieve 50 -60% retain here other than total on the top 3 sieves = pef 40-50% grain diet results in at least 25-30% the pan Still long and functional pef , more so than 4 mm material. Functions as pef sieve, no recommendation for amount to Sortable material, too long, increases time needed for eating;

% 0-5 >50 2017 Miner Miner 10-20 25-30

% 2-8 2013 PSPS 30-50 10-20 30-40

8 4 --- 19 mm Suggested PSPS targets: PSPS targets: Suggested (2017) Miner Institute Sieve

Top Mid 1 Mid 2 Pan

208 209 (47-h in situ) (47-h in situ) corn silage NDF corn silage NDF Importance of rumen digestion: Importance

210 (47-h in situ) (47-h in situ) Grass silage NDF Grass silage NDF

211 (47-h in situ) (47-h in situ) Wheat straw NDF residue residue NDF straw Wheat

212 Opening of cells Leaching of chlorophyll and pigments Fraying of fiber strands Fragmentation, size reduction Buoyancy changes Faster passage, greater intake Microbial degradation Microbial degradation ! ! ! ! ! !

213 Perspectives - 1 Perspectives

214 and rumen pH pH rumen peNDF and uNDF , there is more in the Rumen ﬁll and DMI management x feeding budge2ng Time to response Chewing Perspectives - 2 Perspectives ! ! ! If cow eats more max and min for 0.30 to 0.45% of BW, uNDF240? aﬀects: NDF diges2bility (indiges2bility) rumen, up to a maximum amount amount a maximum to up rumen, ! ! !

215 to being able better close Bottom Line … Bottom model effects of rumen NDF digestibility and indigestibility. We are Exciting time to be feeding forages dairy cattle. Version 7 of CNCPS Other models? Stay tuned… ! ! ! ! !

216 www.whminer.org Thank you! Thank

217 Carbohydrates: Measuring Them and Managing Them in Dairy Cattle Rations

Mary Beth Hall, Ph.D. U. S. Dairy Forage Research Center, USDA-Agricultural Research Center, Madison, WI

If you can’t measure it, you can’t manage it. That saying applies to many things including feed analyses and ration formulation. It’s only been in the last 10 or 20 years that we’ve gotten practical analyses that let us break out feed carbohydrates into fractions that are more nutritionally relevant. And we probably are not done, yet. For the moment, understanding what we can measure and measure accurately, and how the fractions may affect performance is a good start.

Carbohydrate Analyses

Nonfiber carbohydrate (NFC) were intended to describe the most digestible carbohydrates. Calculated by difference as 100 – crude protein – neutral detergent fiber – ether extract – ash, NFC has been in use in some form since the late 1800’s. Where NFC fails is that it treats all of the carbohydrates as though they were nutritionally equivalent, and all the errors and variability in the fractions subtracted go into NFC. One of the errors is the degree to which nitrogen content x 6.25 accurately reflects the mass accounted for by protein; it varies by feedstuff (Jones, 1931). Any feed component not measured by the protein, fiber, fat, or ash analyses is included in NFC, even if they are not carbohydrates: ethanol, organic acids, browning reaction products, neutral detergent-soluble gunk (technical term), etc. How inaccurate NFC is for approximating non-NDF carbohydrates varies by feedstuff. In a case such as molasses that can contain approximately 10% browning reaction products (Binkley and Wolfram, 1953), NFC will be overestimated by at least that amount. Ultimately, use of NFC is not recommended if we can do a better job with more nutritionally accurate fractions.

Current schemes for feed analysis splits carbohydrates into water- soluble carbohydrates (WSC), starch, neutral detergent-soluble fiber, and neutral and acid detergent fibers (Figure 1). This fairly

Figure 1. Carbohydrate analyses. ESC: 80% ethanol- soluble carbohydrates, WSC: water- soluble carbohydrates, NDSF: neutral detergent- soluble fiber, NFC: nonfiber carbohydrates, ADF & NDF: acid and neutral detergent fibers.

218 Water-soluble carbohydrates (WSC). These are very literally the carbohydrates soluble in water and then measured by a broad spectrum carbohydrate analysis (the phenol-sulfuric acid assay). The soluble categorizes the carbohydrates by digestibility characteristics and how rumen microbes utilize them, though there are some differences by carbohydrate type which are also mostly aligned with feed source. carbohydrates have been referred to as “sugars”, but the fraction can contain a great variety of carbohydrates, depending on the feed. The group includes simple sugars (glucose, fructose), disaccharides (sucrose, lactose), short chain carbohydrates (“oligosaccharides” like stachyose and raffinose which are in soybeans), and short and long chain fructans which are found primarily in cool season grasses. Typically, sucrose is used as the sugar standard in the analysis, because it’s the predominant WSC in most of our feeds. But, we need to keep in mind that the phenol-sulfuric acid assay gives different responses for different sugars. So, if another carbohydrate is known to predominate (like lactose in whey permeate), you will get a more accurate WSC analysis if you use that carbohydrate as the standard for that feed.

Besides WSC, 80% ethanol-soluble carbohydrates (ESC) has been used to measure soluble carbohydrate. However, ESC does not include the long chain fructans or lactose, both of which are insoluble in 80% ethanol. At the end of the day, WSC appears to be a better assay to use than ESC because it gives a more complete value for this group of carbohydrates.

“Total sugars as invert” is a value given for molasses. This is not WSC per se, but is a good value for sum of sucrose, glucose, and fructose in molasses. If whey was added to the molasses to help it flow, the value may or may not include lactose, or may count only half of the lactose, depending on the analysis used.

Starch. This polysaccharide made entirely from glucose is commonly analyzed using enzymes (heat-stable, α-amylase and amyloglucosidase) that specifically hydrolyze the α-1,4 and α-1,6 linkages between the glucoses. The enzymatically-released glucose x 0.9 gives the amount of starch. The 0.9 is the mathematical approach to accounting for the molecule of water that is lost from each glucose molecule when the glucoses bond to each other. Any free glucose in the sample should not be counted as starch; it needs to be extracted away or measured in an assay using no enzymes and then subtracted from the glucose measured after digestion with enzymes.

Starch values may be lower than they should be if the assay is run at neutral or close to neutral pH (should be slightly acidic; Dias and Panchal, 1987), if the sample is not finely ground (1 mm abrasion mill, or 0.5 mm cutting mill), or if the sample is not completely gelatinized so that the enzymes can attack the chemical bonds. Starch values will be inflated if free glucose isn’t subtracted, or if the enzymes or run conditions release glucose from carbohydrates other than starch (sucrose can be a problem). Analytical labs should get dry matter basis values of approximately (+/- 2% of dry matter) 100% for corn starch, 90% for glucose, and 0% for sucrose on control samples if the assay is working well.

219 Neutral Detergent-Soluble Fiber (NDSF). Soluble fiber includes polysaccharides such as pectin, mixed linkage beta-glucans and gums that are soluble in neutral detergent and are not digestible by mammalian enzymes. Present assays that specifically analyze for pectins and gums are tedious and expensive and not typically practical to be run on feeds. The NDSF is a by-difference approach to give an approximation of this group of carbohydrates. The soluble carbohydrates are extracted away, starch is measured, and the difference between the 80% ethanol extracted residue and the neutral detergent residue, both corrected for crude protein and ash, minus starch gives NDSF. This assay has the same issues that any by-difference assay has: the errors and variability in the component assays. Large fructans will be found in both WSC and NDSF, but this should only affect cool season grasses.

Fiber. Analysis of neutral detergent fiber (NDF) varies in whether heat-stable, α-amylase or sodium sulfite is added. Amylase removes starch and sulfite breaks disulfide linkages to remove protein. Their combined use gives NDF values that are more hemicellulose, cellulose, and lignin, and less contaminating material. The only advisable reason not to use sodium sulfite is to produce samples for determining neutral detergent insoluble nitrogen (NDIN or NDFCP both expressed as crude protein with N x 6.25). Subtraction of NDFCP from the NDF value is only appropriate when both assays are run using the same reagents.

Acid detergent fiber (ADF) is comprised of cellulose, lignin, and acid detergent insoluble nitrogen (ADIN or ADFCP both expressed as crude protein with N x 6.25). ADIN has been used to estimate undigestible or heat-damaged protein in feeds. However, at least some portion of heat- damaged proteins may be digestible and utilized by the animal (Machacek and Kononoff, 2009).

Ash is the mineral in feedstuffs that remains after incinerating a sample. Ash is important to the discussion of fiber because of its potential to inflate values with a contaminant that is not carbohydrate and has no potential to be fermented by gut microbes. Common methods for analyzing NDF and ADF report results on a with-ash or ash-free basis. Ash in feed samples comes from minerals in plant cells, added minerals, from soil contamination, or from biogenic silica that plants, commonly grasses, naturally incorporate into their structure. “Ash- free” requires that the residue remaining after extraction with neutral or acid detergents be incinerated and the residual ash subtracted so that it is not counted as part of the fiber. “With-ash” leaves that mineral as part of the fiber and so inflates the fiber value and the estimate of potentially fermentable cell wall. Most commercial analyses have given fiber values on a with-ash basis; it improves turnaround time on samples and the contaminating mineral is often a low value with NDF. Soil contamination inflates both NDF and ADF analyses because neither completely solubilizes it. A feed sample heavily contaminated with soil will also show unusually high ash values (approximately 5% or more of dry matter than average values for a feed). If the high ash content is specific to the subsample, the best option is to take and analyze a different, uncontaminated sample. If the feed source itself is high in ash, running the fiber measures on an ash-free basis is the best way to determine the actual carbohydrate content. Because biogenic silica is soluble in neutral detergent but is quantitatively recovered in the residue with acid detergent (Van Soest, 1994), to get an accurate assessment of carbohydrate in ADF, particularly with grasses,

220 ADF should be determined on an ash-free basis, or by running a sample sequentially through the NDF and then ADF analyses.

Managing Carbohydrates In Rations

One of the challenges we have in coming up with firm recommendations for carbohydrate feeding in dairy cattle diets is that “it depends”, and we do not always know on what, and we continue to learn more about how the different fractions function in the cow. How do these things affect the outcome: the amount, particle size, fragility, and fermentability of the fiber sources? Fermentability of the starch? Total protein or ruminally degradable protein (RDP) content of the diet? Types of RDP? And how do all these pieces fit together and interact to determine nutrient supply and animal performance? Here, we will focus on the non-NDF carbohydrates. There is relatively little information available on NDSF and fructans.

Current TMR formulations in the upper Midwest and Northeast use from 3 to 7% WSC, 20 to 30% starch, 7 to 12% NDSF, and 26 to 32% NDF (~75% forage NDF/total NDF). The 2001 Dairy NRC (Table 4-3) had NFC decreasing and total NDF increasing as forage NDF decreased; the NFC in their example would have mainly been starch. Feeding studies have shown that starch isn’t required to provide energy, though it is a convenient and readily available form here in the U.S. A survey of rations that maintained good health and performance in commercial herds was updated with 3 studies that varied starch vs. other NFC and maintained milk production of 75 to 88 lb of milk (Figure 2). Two of the studies only reported starch composition of the diets. The starch, ESC (proxy for WSC), and NDSF in the original survey showed starch increasing, ESC decreasing, and NDSF little changed as forage percentage of the rations increased. Those changes did not necessarily indicate what the ideal diets were, as much as what worked with feeds that were available in given areas. It did agree with our concept that you need to make sure that there is adequate chewable fiber from forage in the ration to maintain ruminal pH when much fermentable starch is fed. However, with the research studies added, the picture is not as clear (gray symbols in graph). The starch, ESC and NDSF contents of the research diets differed a lot from the pattern that showed up on the commercial farms,

Figure 2. Relationship of ESC (proxy for WSC), starch, NDSF and their sum to dietary forage in healthy, high producing herds, NFC and in research studies (gray- shaded symbols). (Hall and Van Horn, 2001; Voelker and Allen, 2003; Hall et al., 2010; Hall and Chase, 2014).

221 but cow performance was still good. Starch (triangles) was higher (35%)or lower (11%), ESC (squares) was higher (12%), and NDSF (circles) was higher (15-19%) on some of the experimental diets. The research rations were in a far narrower range of forage inclusion than were the commercial herd diets. What is interesting is that NFC or the sum of ESC, starch, and NDSF was relatively flat across the herds and studies, averaging 38% of ration dry matter, and ranging primarily from 35 to 42% of dry matter. A side note: diets up to approximately 20% sugars from sugar cane have been fed to Nellore steers at intakes of ~12 lb (Sousa et al., 2014).

Lacking clear targets for how much of the individual carbohydrate types to feed, understanding how they behave in the rumen and can affect animal performance can help us to figure out how to work with them.

Rumen Function

Rumen microbes convert feed carbohydrates into a number of products including organic acids (lactate, acetate, propionate, butyrate, valerate), gases (carbon dioxide and methane), microbial cells, and glycogen (Figure 3). Glycogen is a polysaccharide with a structure very similar to starch that is made and stored internally by both protozoa and bacteria. It may be fermented later by the microbes, or pass from the rumen. There can be a significant flow of glycogen (with potential to digest like starch) to the small intestine even on all forage rations (Branco et al., 1999). So, glycogen production essentially slows down fermentation and acid production, relative to the readily available carbohydrate from which it was formed. A hidden cost of glycogen accumulation: each glucose stored in glycogen has a cost equivalent to 1 ATP to add it to the polysaccharide chain (Ball and Morell, 2003). So, if rumen microbes obtain 3 to 4 ATP from fermenting a carbohydrate (Russell and Wallace, 1988), storing it as glycogen effectively decreases the ATP yield by 25 to 33%, reducing the amount of energy available to drive microbial cell production. Factors affecting glycogen production: • More microbial glycogen is made when greater amounts of rapidly available carbohydrate are present (Prins and Van Hoven, 1977), particularly if there is more relative to their ability to process it and use the energy; this may be an alternative to energy spilling. • More available RDP can decrease glycogen production (McAllan and

Figure 3. Fates of carbohydrates. SI = small intestine, LI = large intestine.

Smith, 1974) and

222 increase the flux of carbohydrate through fermentation, which can also increase lactate production (Counotte and Prins, 1981; Malestein et al., 1984).

Some general observations about the effects of carbohydrates, but that are not always observed: Increased milkfat production with feeding more sugars (Broderick and Radloff, 2004; Broderick et al., 2008; Penner and Oba, 2009). Starch gives more milk protein than other NFC (Broderick and Radloff, 2004; Sannes et al. 2002; Hall et al., 2010).

What we know (the most information is available on starch and sugars…): • Sugars can be very rapidly utilized in the rumen for fermentation but also can be used by microbes to produce substantial amounts of glycogen which slows fermentation and acid production (Thomas, 1960). • Lactose and orchardgrass fructans are taken up more slowly by microbes and produce less glycogen than does glucose (Figure 4; Hall 2016; Hall and Weimer, 2016). Fructans appear to ferment more rapidly than lactose. • Starch can also be converted to microbial glycogen. • Sucrose has been reported to yield less microbial protein than starch (Hall and Herejk, 2001; Sannes et al., 2002) even when rumen pH was not an issue. The reduction in protein with sucrose may be related to greater glycogen production and less energy available for microbial cell growth. • Given glucose as a substrate, ruminal microbes prefer to use amino nitrogen (amino acids, peptides) rather than ammonia or urea (Hristov et al. 2005; Hall, 2017). ! For the milk protein picture, it is possible that this is related to relatively more glycogen and less microbial protein being produced with sucrose and glucose than with starch. There’s also the possibility that the type of RDP available in the rumen will modify the results. As for lactose, reports from the field suggest that this more slowly fermenting sugar can be substituted for corn grain.

Figure 4. When fermented with ruminal microbes in vitro, lactose disappears more slowly than glucose, and the microbes make more glycogen from glucose. (Hall, 2016).

223 • Fermentation of sugars tends to produce more butyrate than does starch, and may yield more lactate (Strobel and Russell, 1986). • Some species of glucose-utilizing microbes perform biohydrogenation on fatty acids in the rumen (Butyrivibrio fibrosolvens; McKain et al., 2010). ! Milkfat production increases associated with sugar feeding may be related to increased biohydrogenation of fatty acids to forms that will not cause milkfat depression, or the increased butyrate influences milkfat production. Occasionally, modifying dietary protein can increase milkfat. Is this due to increasing the fermentation and growth of the microbes that biohydrogenate or make butyrate?

Additional note on sugars vs. starch: In a ruminal acidosis induction study, crushed wheat caused more severe ruminal lesions than did molasses. With crushed wheat, the ruminal pH continued to fall over 120 hours reaching 3.93, whereas with molasses, it fell to 4.76 by 24 hours and then started to rise (Randhawa et al., 1982). This may be due to the molasses moving with liquid vs. starch being a solid that would not as readily pass from the rumen. And possibly due to formation of glycogen with the more readily available sugars.

Additional note on starch: We think of starch as a major source of propionate, but that may only be true when substantial amounts of starch or fermented feeds with lactate are fed (Murphy et al., 1982). The increase in ruminal propionate concentrations commonly associated with increased starch feeding may be a function of a change in the microbial population and pH rather than a characteristic of starch itself (Baldwin et al., 1962; Mackie and Gilchrist, 1979).

Influence of Protein on Ruminal Carbohydrate Use • Increasing availability of RDP increases the yield of microbial protein per amount of carbohydrate (Figure 5; Argyle and Baldwin, 1989). Possibly by decreasing glycogen production and providing a needed nutrient for growth. • Increasing RDP may increase ruminal organic acid concentration and decrease pH (Aldrich et al., 1993), and increase ruminal lactic acid concentration (Hall, 2013). Again, this may be due to decreasing glycogen production and increasing the flux of carbohydrate through fermentation. • Increasing RDP can remove the depressing effect of nonfiber carbohydrates on fiber fermentation (Heldt et al., 1999).

Figure 5. Microbial yields (as micrograms of RNA/mg of carbohydrate added) for mixed ruminal microbes after 6 hours of fermentation. The “mg/L” values are for the amount of amino acids + peptides added; media for all treatments contained 3.6 millimolar ammonia. (Argyle and Baldwin, 1989).

224 Thoughts on Applications

The different fractions of carbohydrate have distinctly different fermentation and digestion characteristics that may have benefits or drawbacks under different scenarios. A key seems to be providing enough readily digestible carbohydrate of whatever types without creating problems in the rumen (acidosis) and the rest of the digestive tract (enteritis). This means paying attention to adequacy of the physical form of the ration and how that fits with the different carbohydrate components. That requires observing the cows for signs that they are functioning well with their ration. That protein supply can modify how rumen microbes process carbohydrate tosses in another variable that we have not explored well. Use RDP without overfeeding as a modifier of microbial growth or acid production in the rumen? Potentially. We still have substantial gaps in our knowledge regarding how best to utilize different carbohydrate sources in dairy cattle rations.

References

Aldrich, J. M., L. D. Muller, G. A. Varga, and L. C. Griel, Jr. 1993. Nonstructural and carbohydrate effects on rumen fermentation, nutrient flow, and performance of dairy cows. J. Dairy Sci. 76:1091- 1105. Argyle, J. L., and R. L. Baldwin. 1989. Effects of amino acids and peptides on rumen microbial growth yields. J. Dairy Sci. 72:2017- 2027. Baldwin, R. L., W. A. Wood, and R. S. Emery. 1962. Conversion of lactate-C14 to propionate by the rumen microflora. J. Bacteriol. 83:907-913. Ball, S. G., and M. K. Morell. 2003. From bacterial glycogen to starch: understanding the biogenesis of the plant starch granule. Annu. Rev. Plant Biol. 54:207-233. Binkley, W. W., and M. L. Wolfram. 1953. Composition of cane juice and cane final molasses. Scientific report series No. 15. Sugar Research Foundation, Inc. New York. Originally published in Advances in carbohydrate chemistry, Vol. III, Academic Press, Inc. Branco, A. F, D. L. Harmon, D. W. Bohnert, B. T. Larson, and M. L. Bauer. 1999. Estimating true digestibility of nonstructural carbohydrates in the small intestine of steers. J. Anim. Sci. 77:1889-1895. Broderick,G. A., N. D. Luchini, S. M. Reynal, G. A. Varga, and V. A. Ishler. 2008. Effect on Production of Replacing Dietary Starch with Sucrose in Lactating Dairy Cows. J. Dairy Sci. 91:4801-4810. Broderick, G. A., and W. J. Radloff. 2004. Effect of Molasses Supplementation on the Production of Lactating Dairy Cows Fed Diets Based on Alfalfa and Corn Silage. J. Dairy Sci. 87:2997-3009. Counotte, G. H. M., and R. A. Prins. 1981. Regulation of lactate metabolism in the rumen. Vet. Res. Comm. 5:101-115. Dias, F. F., and D. C. Panchal. 1987. Maltulose formation during saccharification of starch. Starch 39: 64-66. Hall, M. B. 2013. Dietary starch source and protein degradability in diets containing sucrose: effects on ruminal measures and proposed mechanism for degradable protein effects. J. Dairy Sci. 96:7093-7109. Hall, M. B. 2016. Utilization of lactose by mixed ruminal microbes is affected by nitrogen type and level and differs from utilization of glucose. J. Dairy Sci. 99 (E- Suppl. 1): 774.

225 Hall, M. B. 2017. Nitrogen source and concentration affect utilization of glucose by mixed ruminal microbes in vitro. J. Dairy Sci. In Press. https://doi.org/10.3168/jds.2016-12091 Hall, M. B., and L. E. Chase. 2014. Responses of late-lactation cows to forage substitutes in low-forage diets supplemented with by-products. J. Dairy Sci. 97:3042-3052. Hall, M. B., and C. Herejk. 2001. Differences in yields of microbial crude protein from in vitro fermentation of carbohydrates. J. Dairy Sci.84:2486-2493. Hall, M. B., and H. H. Van Horn. 2001. How Should We Formulate For Non- NDF Carbohydrates? Proc. 12th Annual Florida Ruminant Nutrition Symposium, Gainesville, FL. pp. 44-50. Hall, M. B., and P. J. Weimer. 2016. Divergent utilization patterns of grass fructan, inulin, and other nonfiber carbohydrates by ruminal microbes. J. Dairy Sci. 99:245-257. Hall, M. B., C. C. Larson, and C. J. Wilcox. 2010. Carbohydrate source and protein degradability alter lactation, ruminal, and blood measures. J. Dairy Sci. 93:311-322. Heldt, J. S., R. C. Cochran, G. L. Stokka, C. G. Farmer, C. P. Mathis, E. C. Titgemeyer, and T. G. Nagaraja. 1999. Effects of different supplemental sugars and starch fed in combination with degradable intake protein on low-quality forage use in beef steers. J. Anim. Sci. 77:2793-2802. Hristov, A. N., J. K. Ropp, K. L. Grandeen, S. Abedi, R. P Etter, A. Melgar, and A. E. Foley. 2005. Effect of carbohydrate source on ammonia utilization in lactating dairy cows. J. Anim. Sci. 83:408- 421. Jones, D. B. 1931. Factors for converting percentages of nitrogen in foods and feeds into percentages of proteins. Circular No. 183. USDA, Washington, DC. Machacek, K J, and P. J. Kononoff P J. 2009. The relationship between acid detergent insoluble nitrogen and nitrogen digestibility in lactating dairy cattle. Prof Anim Sci 2009; 25:701-708. Mackie, R. I., and F. M. C. Gilchrist. 1979. Changes in lactate- producing and lactate-utilizing bacteria in relation to pH in the rumen of sheep during stepwise adaptation to a high concentrate diet. Appl. Environ. Microbio. 38:422-430. Malestein, A., A. T. van’t Klooster, R. A. Prins, and G.H.M. Counotte. 1984. Concentrate feeding and ruminal fermentation. 3. Influence of concentrate ingredients on pH, on DL-lactic acid concentration in the rumen fluid of dairy cows and on dry matter intake. Neth. J. Agric. Sci. 32:9-21. McAllan, A. B., and R. H. Smith. 1974. Carbohydrate metabolism in the ruminant: Bacterial carbohydrates formed in the rumen and their contribution to digesta entering the duodenum. Br. J. Nutr. 31:77-88. McKain, N., K. J. Shingfield, and R. J. Wallace. 2010. Metabolism of conjugated linoleic acids and 18 : 1 fatty acids by ruminal bacteria: products and mechanisms. Microbiology 156:579-588. Murphy, M. R., R. L. Baldwin, and L J. Koong. 1982. Estimation of stoichiometric parameters for rumen fermentation of roughage and concentrate diets. J. Anim. Sci. 55:411-421. Penner, G. B., and M. Oba. 2009. Increasing dietary sugar concentration may improve dry matter intake, ruminal fermentation, and productivity of dairy cows in the postpartum phase of the transition period. J. Dairy Sci. 92:3341-3353. Prins, R. A., and W. Van Hoven. 1977. Carbohydrate fermentation by the rumen ciliate Isotricha prostoma. Protistologica 13:549-556.

226 Randhawa,S.S., L. N. Das, and S. K. Misra. 1982. Comparative biochemical and pathological studies on acute ruminal acidosis induced by molasses and grain feeding in buffalo calves (Bubalus bubalis). Acta Veterinaria Academiae Scientiarum Hungaricae 30:257- 264. Russell, J. B., and R. J. Wallace. 1988. Energy yielding and consuming reactions. Pages 185–215 in The Rumen Microbial Ecosystem. P. N. Hobson, ed. Elsevier Applied Science, London, UK. Sannes, R. A., M. A. Messman, and D. B. Vagnoni. 2002. Form of Rumen- Degradable Carbohydrate and Nitrogen on Microbial Protein Synthesis and Protein Efficiency of Dairy Cows. J. Dairy Sci. 85:900-908. Sousa, D. O., B. S. Mesquita, J. Diniz-Magalhães, I. C. S. Bueno, L. G. Mesquita, and L. F. P. Silva. 2014. Effect of fiber digestibility and conservation method on feed intake and the ruminal ecosystem of growing steer. J. Anim. Sci. 92:5622–5634. Strobel, H. J. and J. B. Russell. 1986. Effect of pH and energy spilling on bacterial protein synthesis by carbohydrate-limited cultures of mixed rumen bacteria. J. Dairy Sci. 69:2941-2947. Thomas, G. J. 1960. Metabolism of the soluble carbohydrates of grasses in the rumen of the sheep. J. Agric. Sci. 54:360-372. Van Soest, PJ. 1994. Nutritional ecology of the ruminant. 2nd edition. Ithaca (NY): Cornell University Press. Voelker, J. A., and M. S. Allen. 2003. Pelleted beet pulp substituted for high-moisture corn: 1. Effects on feed intake, chewing behavior, and milk production of lactating dairy cows. J. Dairy Sci. 86:3542- 3552.

227 2017 California Animal Nutrition Conference - Proceedings

Biology, Behavior, and Management of Flies associated with Animal Agriculture

Dr. Alec C. Gerry Professor of Veterinary Entomology Department of Entomology University of California at Riverside

FLY IMPACTS:

Of the many thousands of species of true flies (order Diptera), only a few have a close association with animals. Of particular significance are those fly species that complete their immature development in animal feces and are therefore called “filth flies” (Table 1). Wet feces are an excellent developmental environment for these filth fly species, while also supporting the survival of many disease-causing pathogens. In addition to contact with feces during immature development, adult flies will visit feces to feed and to deposit eggs. To acquire nutrients needed for adult survival and egg development, some of these fly species also feed readily on available animal feed, as well as on sweat or other animal body secretions.

Two species (stable fly and horn fly) feed on animal blood as adult flies, and their painful bites result in reductions in animal weight gains and milk yields due to animal discomfort. These production impacts are largely the result of reduced feed and water consumption coupled with increased animal activity and animal heat stress as harassed animals exhibit a number of defensive behaviors to avoid the painful bites. One of these defensive behaviors, cattle bunching, is often observed when biting stable flies become numerous (Weiman et al. 1992). Overall losses to the animal industry in the United States from these blood-feeding filth flies are estimated at over $2 billion annually (Drummond 1987, Taylor et al. 2012).

Filth flies, particularly house flies and flies in the blow fly family (Calliphoridae), have been implicated in the transmission of a phylogenetically diverse group of human and animal pathogens associated with feces and which cause enteric illness (Greenberg 1973, Sasaki et al. 2000, Chakrabarti et al. 2007, Ahmed et al. 2007). While the extent to which filth flies are responsible for the transmission of pathogens to animals is unclear, there is little doubt that filth flies are responsible for the dispersal and transmission of numerous enteric pathogens among animals, and may also move these enteric pathogens to nearby pre-harvest human food plants (Talley et al. 2012).

Table 1: Common filth fly species in the U.S. and their developmental habitat

228 Filth Fly Species Immature Habitat House fly (Musca domestica) Feces, food waste, sewage sludge Face fly (Musca autumnalis) Fresh cattle feces Little house fly (Fannia canicularis) Poultry feces Stable fly (Stomoxys calcitrans) Cattle feces, hay waste, green waste, sewage sludge Horn fly (Haematobia irritans) Fresh cattle feces Blow flies (Family: Calliphoridae) Food waste, carrion, feces

FLY LIFE CYCLE:

Filth flies undergo complete metamorphosis with egg, larva, pupa, and adult stages in their development (Fig. 1). Female flies deposit eggs in animal waste or other moist organic material where the larvae complete their development feeding on the bacteria or organic material associated with the developmental site. Immature filth flies pass through three larval instars (stages), growing larger with each successive instar. At the end of the third instar stage, the larvae typically enter a “wandering stage”, where they leave their development sites in search of a dry and protected location to pupate. The rate of fly development from egg to adult, as with all insects, is dependent upon temperature. Under summertime conditions throughout the United States, many filth flies can complete development from egg-to-adult in as little as 6-8 days (Moon 2009).

229 FLY MANAGEMENT

Although adult flies are the cause of nuisance and the carriers of pathogens, the larval stages should be the prime target for control efforts. Because elimination of suitable larval habitat will prevent subsequent production of adult flies, sanitation is the first line of defense against filth flies. At animal production facilities, filth flies are best managed through practices designed to reduce the attractiveness of manure to egg-laying female flies, as well as practices designed to reduce the quantity and quality of manure that might be suitable for larval development. Frequent collection and removal of wet animal manure to offsite sanitary landfills can be a suitable strategy, but is often cost prohibitive for a large animal facility. More typical is the frequent collection of manure by scraping animal housing areas or the capture of manure solids in wash water followed by composting of manure in piles or windrows to reduce the surface area of the manure available to developing flies. In cases where frequent manure removal is not an option, every effort should be made to keep the manure as dry as possible by good ventilation and the prompt repair of water leaks that wet the collected manure and result in the creation of larval development “hot spots”.

Many natural enemies of flies are generally present wherever these flies occur. Fly eggs and larvae are eaten by predators, fly pupae are killed by parasitic wasps, and adult flies have viral and fungal diseases that shorten their lifespan and reduce their egg output (Geden 2006). These natural enemies provide “free” fly control and can be encouraged by avoiding broadcast insecticide applications and by efforts to keep manure accumulations as dry as possible.

Chemical control methods may be needed on animal facilities when sanitation measures fail and fly surveillance shows rapidly increasing numbers of adult flies exceeding a treatment threshold (Gerry 2011). Adults of all filth fly species may be reduced using insecticides applied as liquid sprays or fogs to locations such as animal housing or shade structures where the adults of these fly species tend to rest. Application of insecticides as sprays or liquid pour-ons directly applied to animals, or in insecticide impregnated ear tags can be used to reduce blood-feeding flies (Gerry et al. 2007). Flies exposed to the same chemical class continuously will surely and rapidly become resistant to products in the same chemical class. Consultation with local extension staff or other knowledgeable personnel can help identify which insecticides are still effective for these pests in a particular geographic area.

Recent efforts to reducing fly biting pressure on cattle have focused on the application of low-toxicity repellents applied directly to animals. Some plant essential oils may even prove suitable for use on organic facilities. Two low-toxicity repellents, geraniol and a mixture of short chain fatty acids (C8-C9-C10) have shown considerable promise in this regard (Mullens et al. 2009, Zhu et al. 2014). Additional studies with these repellents that were recently conducted in California and North Carolina will be discussed.

Given that this presentation on fly management will be given to researchers and others interested in animal nutrition, I would be remiss if I did not mention the “feed-through” products that are registered for use to control flies that develop in animal feces. These products pass through the animal digestive system and into the animal feces where they are present in sufficient concentrations to prevent fly development in the feces. A search of the on-line VetPestX pesticide registration database for “feed-through” products to control flies on cattle

230 yields eight different products with only two different active ingredients (diflubenzuron and tetrachlorvinphos) representing two different chemical classes. This searchable database of insecticides registered for use on animals was recently produced by my laboratory and is available to search for free by visiting the Insect Pests of Animals website at http://veterinaryentomology.ucr.edu/ and then selecting the VetPestX tab. Recently, a newer benzoylphenyl urea insecticide (Novaluron) has also showed promise as a feed-through for control of biting flies by disrupting insect development in feces of treated animals (Lohmeyer et al. 2014). I will discuss the availability and use of feed-through insecticides during the presentation.

REFERENCES:

Ahmad, A; Nagaraja, T.G.; Zurek, L. Transmission of Escherichia coli O157: H7 to cattle by house flies. Prevent. Vet. Med. 2007, 80, 74-81.

Chakrabarti, S.; King, D.J.; Afonso, C.; Swayne, D.; Cardona, C.J.; Kuney, D.R.; Gerry, A.C. Detection and isolation of exotic Newcastle disease virus from field-collected flies. J. Med. Entomol. 2007, 44, 840-844.

Drummond, R.O. Economic aspects of ectoparasites of cattle in north America. In The Economic Impact of Parasitism in Cattle; Leaning, W.H.D., Guerrero, J. Eds.; Proceedings of a Symposium, XXIII World Veterinary Congress, Montreal, Canada. 1987; 9-24.

Geden, C.J. 2006. Biological control of pests in livestock production. In Implementation of Biocontrol in Practice in Temperate Regions - Present and Near Future. Hansen, L. S.; Enkegaard, A.; Steenberg, T.; Ravnskov, S., Eds. Ministry of Food, Agriculture and Fisheries, Danish Institute of Agricultrual Sciences. 2006.

Gerry, A.C.; Peterson, N.G.; Mullens, B.A.; Predicting and controlling stable flies on California dairies. University of California ANR Publication 8258, 2007.

Gerry, A.C.; Higginbotham, G.E.; Periera, L.N.; Lam, A.; Shelton, C.R. Evaluation of surveillance methods for monitoring house fly abundance and activity on large commercial dairy operations. J. Econ. Entomol. 2011, 104, 1093-1102.

Greenberg, B. Flies and disease, Vol. II. Princeton Univ. Press; Princeton, New Jersey, 1973.

Lohmeyer, K.H.; Pound, J.M.; Yeater, K.M.; May, M.A. Efficacy of novaluron as a feed-through for control of immature horn flies, house flies, and stable flies (Diptera: Muscidae) developing in cow manure. J. Med. Entomol. 51, 873-877. 2014.

Moon, R.D. Muscid Flies (Muscidae). In Medical and Veterinary Entomology; Mullen, G., Durden, L. Eds.; Academic Press: San Diego, CA. 2009.

231 Mullens, B.A;, Reifenrath, W.G.; Butler, S. M. Laboratory trials of fatty acids as repellents or antifeedants against house flies, horn flies and stable flies (Diptera: Muscidae). Pest Manag. Sci. 65, 1360-1366. 2009.

Sasaki, T.; Kobayashi M.; Agui, N. Epidemiological potential of excretion and regurgitation by Musca domestica (Diptera: Muscidae) in the dissemination of Escherichia coli O157:H7 to food. J. Med. Entomol. 2000, 37, 945-949.

Talley, J.L.; Wayadande, A.C.; Wasala, L.P.; Gerry, A.C.; Fletcher, J.; DeSilva, U.; Gilliland, S.E. Association of Escherichia coli O157:H7 with filth flies (Muscidae and Calliphoridae) captured in leafy greens fields and experimental transmission of E. coli O157:H7 to spinach leaves by house flies (Diptera: Muscidae). J. Food Protection 2009, 72, 1547- 1552.

Taylor, D.B.; Moon, R.D.; Mark, D.R. Economic impact of stable flies (Diptera: Muscidae) on dairy and beef cattle production. J. Med. Entomol. 2012, 49, 198-209.

Weiman, G.A.; Campbell, J.B.; Deshazer, J.A.; Berry, I.L. Effects of stable flies (Diptera:Muscidae) and heat stress on weight gain and feed efficiency of feeder cattle. J. Econ. Entomol. 1992, 85, 1835-1842.

Zhu, J.J.; Brewer, G.J.; Boxler, D.J.; Friesen, K.; Taylor, D.B. Comparisons of antifeedancy and spatial repellency of three natural product repellents against horn flies, Haematobia irritans (Diptera: Muscidae). Pest Manag. Sci. 70, DOI 10.1002/ps.3960. 2014.

232 233 Well-formulated, palatable ration Feed available 24/7 Competition doesn’t limit feed access Water availability No restrictions on resting or ruminating activity ! ! ! ! ! Do you provide the perfect Do you provide the dining experience?

234 Crepuscular Allelomimetic Competitive " " " Natural feeding behavior of dairy cows: Does your “dining” environment accommodate or frustrate these basic feeding drives? Know your customer… Know your customer… " "

235 Focus on diet Focus on diet and formulation feeding environment

236

(Campbell, 2016) 2016) (Campbell, Focus on Rumen pH Fiber, and Feed Access: and Feed Access: Fiber, Stocking Density, Dietary Density, Stocking

237 (S) 2.3 4.8 3.5 3.2 3.5 1.1 39.7 41.9 Straw … … 6.9 4.8 3.5 3.2 39.7 41.9 (NS) No Straw Ingredient composition composition Ingredient DM) (% of ration Item Corn silage Hay crop silage chopped Wheat straw, Citrus pulp, dry Whole cottonseed, fuzzy Soybean meal, 47.5% solvent Molasses Concentrate mix

238 S 9.7 8.1 5.7 14.9 15.1 30.1 25.5 74.3 20.1 NS 8.5 7.4 5.9 13.1 15.0 30.8 25.0 73.3 18.8

1.18 mm uNDFom uNDFom Chemical composition Chemical composition DM) (% of ration Item CP NDF Starch Sugar Ether Extract % of starch 7-h starch digestibility, peNDF 30-h 240-h

239 Diet Diet 0.03 0.01 SD

0.06 <0.01 NS 0.58 4.12 142% S 0.34 2.77 NS 0.38 2.29 100% S 0.19 1.90 < 0.09). < 0.09).

P Stocking density, dietary dietary density, Stocking and rumen pH fiber, Associated with 6% less lying time (~50 min/d) and 7% less recumbent rumination (~30 min/d). and 7% less recumbent rumination (~30 min/d). # *Interaction ( *Interaction AUC<5.8, pH units x h/d* pH<5.8, h/d*

240 1 0.9 0.8 0.7 0.7 = 0.44 0.6 R ² 142% Stocking Density Density 142% Stocking y = -20.701x + 21.056 + -20.701x = y 0.5 (h/d < pH 5.8) (h/d < pH 5.8) 8 6 4 2 0 12 10 1 0.95 pH 0.9 0.85 0.8 0.75 0.75 0.7 0.7 = 0.01 Rumination in stalls and in stalls Rumination ruminal R ² 0.65 100% Stocking Density Density 100% Stocking y = 2.7886x - 0.3073 - 2.7886x = y 0.6 6 5 4 3 2 1 0

241 1.27 0.63 SEM R 8.77 2.55 142% (Campbell et al., (Campbell et NR 6.78 1.73 R 5.23 1.24 100% NR 6.62 1.66

Feed access and stocking rumen pH density: 2016) Variable pH < 5.8, h/d Time AUC < 5.8 pH, pH x unit

242 , 1 , R. J. Grant 1

, H. M. Dann 1 , and D. M. Barbano 3 , K. W. Cotanch 3 Cornell University, Ithaca, NY Ithaca, Cornell University, 3 University of Vermont, Burlington, VT Burlington, of Vermont, University faLy acid synthesis synthesis acid faLy de novo 2 L. E. Chase , C. Melilli *1,2 William H. Miner Agricultural Research Ins7tute, Chazy, NY NY Chazy, Ins7tute, Research William H. Miner Agricultural 1 in Northeastern US dairy herds Eﬀect of nutriHon and management M. E. Woolpert pracHces on

243 (Woolpert et al., 2015) al., et (Woolpert

Why the interest in de novo interest Why the acids? fatty

244 g n i LDN LDN 116% k c o HDN HDN 111% t 5x 110% 110% P = 0.08 s r Mean e

Stall stocking density ≤ w y t o i l s e n v e LDN LDN a 15.7 in d h s HDN HDN P = 0.06 19.7 in 10x d r e Mean Bunkspace ≥ 18 in/cow h N 8 6 4 2 0

12 10 D Odds RaHo RaHo Odds H

245 k l i M

s d t n n a

e t n n o e p m m e o g C a n a M

246 e 2.9 r LDN LDN o 4.6

m HDN HDN y P = 0.07 d 11x l t e (Tiestall; 2014) (Tiestall; n e Mean Feed delivery ≥ 5x e f s u d q r 1.4 LDN LDN e e r f h 5x P = 0.07 1.7 HDN HDN N (Freestall; 2015) D Twice a day feeding Twice Mean H

8 6 4 2 0

14 12 10 Odds RaHo RaHo Odds

247

LDN LDN 21.4 e e b r i o f 26.8 HDN HDN P < 0.01 e 10x m v i d t Mean e c peNDF ≥ 21% of DM e e f f f s LDN LDN 34.6 e d r y l e l 5x 37.3 HDN HDN P = 0.03 a h c i N s NDF ≥ 35% of DM D Mean y H h

8 6 4 2 0

12 10 p RaHo Odds

248 Cows naturally have Cows naturally aggressive feeding drive … , 1999) 1999) , (Hansen and Pallesen

249 a a 40.1 43.8 2x/h 65.3 1.63 b b (Armstrong et al., 2008) al., et (Armstrong 41.4 45.3 1x/h 61.3 1.48 lb / Fed 3x/day lb /d lb /d " 1 to 2 hours post-feeding is most compeHHve; most displacements hour per once versus 2 hours ﬁrst for ½ hour each Push-up Feed push-up " " Item Item DMI, Milk, Milk/DMI, % of cows Lying in stall,

250 What Naturally Stimulates Stimulates Naturally What Feeding Behavior?

251 (Sova et al., 2013) al., (Sova et 1x versus 2x TMR feeding

252 Refusal amount and Refusal amount … sorting

253 Two percent feed refusals: feed refusals: Two percent like… looks it What

254 How long can the feed How long can the be empty? bunk

255

Effect of empty Effect (Matzke and Grant, 2003) and Grant, (Matzke Case Study: Case Study: -bunk time time -bunk

256 257 No fun being the cow in fun being the No middle … the

258 P >0.05 >0.05 >0.05 >0.05 <0.05 <0.05 <0.001 <0.001 5 5 8 11 LPF Alone 2 4 3 1 Equal Equal choice choice 5 3 1 0 HPF Dominant Dominant low-palatability feed alone low-palatability cow a dominant to feed next high-palatability " " Compared 30, 24, 18, and 12 in of bunk space and preference and 12 in of bunk 18, 24, Compared 30, for: offer choices to Y-maze testing 30 24 18 12 " " Table for one? 2012) al., (Rioja-Lang et (in) Space Space

259 Are 24 in/cow enough?

260 Guarding the water trough: trough: water Guarding the production and milk water

261 (≥24 in/cow) in/cow) (≥24 Management that enhances rest and ruminaHon Management that enhances rest and ruminaHon Feed available on demand the bunk Consistent feed quality/quanHty along stocking density ≤100% Bunk TMR fed 2x/day post-feeding 2 hours on focused Push-ups ~3% feed refusal target never) empty no more than 3 h/d (ideally Bunk The Perfect Dining Experience? The Perfect Recommended Feeding Management " " " " " " " "

262 www.whminer.org Thank you! Thank

263

TECHNICAL SYMPOSIUM SPEAKERS

Tanya Gressley, Ph.D. is an Associate Professor in the Department of Animal and Food Sciences at the University of Delaware. Tanya received her BS and MS degrees in Animal Science from the University of Maryland and a Ph.D. in Dairy Science from the University of Wisconsin. Tanya’s research program focuses on nutrition and nutritional immunology. Her primary area of interest is the impact of intestinal digestion on dairy cattle health and digestion. Recent studies have used abomasal infusions of fermentable carbohydrates to mimic excessive hindgut fermentation that accompanies rumen acidosis. Current work is focusing on quantifying relationships among intestinal leukocyte populations, intestinal inflammation, and mucosal micrabiome. A secondary area of work is the use of dietary supplements to improve animal health and performance. Supplements evaluated in recent projects include egg yolk antibodies, rumen protected trace minerals, and rumen protected amino acids. Tanya’s primary teaching responsibilities are junior/senior level undergraduate Dairy Production and Lactational Physiology courses. She additionally teaches a freshman animal science lab, an honors freshman animal science course, and a freshman animal handling course.

Nicholas Gable, Ph.D. obtained his Bachelors of Agricultural Science from La Trobe University, Melbourne, Australia. In 2005, he received his Ph.D. degree in Animal nutrition and physiology also from La Trobe University. Upon completion of his Ph.D., he conducted postdoctoral research in the USA at both Purdue and Iowa State Universities. Here he worked on evaluating sources of n-3 fatty acids (docosahexaenoic acid and eicosapentaenoic acid) in nursery-finisher pig production and using the pig as a biomedical model. In 2008 he joined the Animal Science Department at ISU as an assistant professor in fundamental swine nutrition and metabolism. Presently, Dr. Gabler has an active and diverse research program that focuses on understanding and improving swine feed efficiency and intestinal physiology at the basic and applied, cellular and whole animal levels. His research program can be divided into four areas: (1) Understanding the physiology and molecular pathways that define feed efficiency differences in swine; (2) Gastrointestinal physiology (integrity and function) of swine; (3) Using the pig as a biomedical model or dual purpose research (livestock and human application); and (4) Understanding the impact of disease and poor health on metabolism, nutrient requirements and tissue accretion. This later research has been using PRRS and PED virus challenge models to study how health challenges alter pig productivity and nutrition. Altogether, these research areas are developing into a highly productive, successful, integrated, hypothesis driven programs. Over the last six years, Dr. Gabler has graduated

264 four M.S. students and three Ph.D. students from his program. He has been active as an author and co-author and has published over 45 peer-reviewed journal articles, a review paper and a book chapter since August 2008. His research is making important scientific contributions to swine production, nutrition and health.

Duarte E. Díaz, Ph.D. holds a M.S. and a Ph.D. in nutrition from North Carolina State University. His research for the past 20 years has focused on the effects of mycotoxins on agriculture. Dr. Diaz has given over 40 invited presentations around the world and has published over 70 articles in scientific journals, proceedings and popular press magazines. In 2005 Dr. Diaz served as editor of a publication that focused on the applied impact of mycotoxins on agriculture titled “The Mycotoxin Blue Book” (Nottingham University Press). The book has sold over 5,000 copies and is widely considered an important reference on the subject. Dr. Diaz has worked in Academia at several institutions including Utah State University, University of Bologna and the Catholic University of the Sacred Heart in Italy. In 2015, after several years working in the private sectors, Dr. Diaz joined the faculty at the University of Arizona as an Associate Professor and Dairy Extension Specialist.

Paige Gott, Ph.D. is a Ruminant Technical Manager for BIOMIN America, Inc. She received both her B.S. and M.S. degrees in Animal Sciences as well as her Ph.D. in the College of Veterinary Medicine from The Ohio State University. Paige focused on udder health and transition cow management during her graduate programs. Dr. Gott is a Ruminant Technical support manager for BIOMIN and is specializing in mycotoxin risk management. Paige resides in Coshocton, Ohio with her fiancé Chip.

265

CANC SPEAKERS

Frank Mitloehner, Ph.D. - Keynote Speaker - is a Professor and Air Quality Specialist in Cooperative Extension in the Department of Animal Science at the University of California, Davis. He received his MS degree in Animal Science and Agricultural Engineering from the University of Leipzig, Germany, and his PhD degree in Animal Science from Texas Technical University. Dr. Mitloehner is an expert for agricultural air quality, livestock housing and husbandry. Overall, he conducts research that is directly relevant to understanding and mitigating of air emissions from livestock operations, as well as the implications of these emissions for the health and safety of farm workers and neighboring communities. Dr. Mitloehner has served as chairman of a global United Nations Food and Agriculture Organization (FAO) partnership project to benchmark the environmental footprint of livestock production. He served as workgroup member on the President’s Council of Advisors on Science and Technology (PCAST) and as member on the National Academies of Science Institute of Medicine (IOM) committee on “A Framework for Assessing the Health, Environmental, and Social Effects of the Food System”.

Carl Old, Ph.D., received a B.S. and Ph.D. from the University of California, Davis. For the past 35 years he has worked as a ruminant nutritionist. Old resides near LeGrand, California.

Ralph Ward is the founder and owner of Cumberland Valley Analytical Services (CVAS), one of the largest forage testing labs in the United States. A graduate of Virginia Polytechnic Institute and State University, Mr. Ward was a dairy herdsman and then spent a number of years involved in on-farm dairy nutrition work. He started CVAS in 1992 as he saw a need for more extensive forage diagnostic services for dairy nutritionists and their clients. With a focus on chemistry analysis and application of non-traditional forage evaluation techniques, CVAS has grown to over 100 employees in three U.S. locations. Mr. Ward was the first to commercialize the use of the "fermentation analysis" in the U.S. and is one of the leaders in the development of in vitro fiber and starch digestibility services. With one of the largest and most comprehensive sets of forage NIR equations and supporting information management technology, Mr. Ward is focused on the establishment of a global NIR forage and feed evaluation network. As part of this network CVAS supports multiple U.S. lab operations as well as operations in Italy, Chile, Argentina, Uruguay, Mexico, British Columbia, Ontario, Quebec, Australia, Japan, China, and South Africa with labs soon to come on line in the U.K. and Germany.

266

Rick Grant, Ph.D. was raised on a dairy farm in northern New York State. He received a B.S. in Animal Science from Cornell University, a Ph.D. from Purdue University in ruminant nutrition, and held a post-doctoral position in forage research at the University of Wisconsin-Madison from 1989 to 1990. From 1990 to 2003, Rick was a professor and extension dairy specialist in the Department of Animal Science at the University of Nebraska in Lincoln. Since February of 2003, he has been President of the William H. Miner Agricultural Research Institute in Chazy, NY, a privately funded educational and research institute focused on dairy cattle, equine, and crop management. Rick’s research interests focus on forages, dairy cattle nutrition, and cow behavior. He has been the recipient of the Pioneer Hi-Bred International Forage Award in 2010 and the Nutrition Professionals Applied Dairy Nutrition Award in 2015.

Mary Beth Hall, Ph.D. is a research animal scientist at the U.S. Dairy Forage Research Center part of the USDA - Agricultural Research Service in Madison, WI. The main areas of her work are feed carbohydrates, their analysis, digestion and use by dairy cattle and their microbes, with special attention paid to carbohydrates in forages and by-product feeds. Over the years, she has worked on dairy farms, as a sales representative for a feed company in the northeastern United States, and as a county agent for Cooperative Extension working on nutrition and management with commercial dairy farms in New York State. She was on faculty working in dairy nutrition extension and research at the University of Florida for 8 years. She received her Animal Science degrees from Cornell University (BS and PhD) and Virginia Tech (MS). She presently lives in rural Wisconsin with her husband (Stu), and a varied pack of dogs.

Alec C. Gerry, Ph.D., Professor and Cooperative Extension Specialist, Department of Entomology, University of California at Riverside. Dr. Gerry received his B.A. Biology, UC Berkeley (1990) and Ph.D. Medical & Veterinary Entomology, UC Riverside (1999). Dr. Gerry has been with the University of California at Riverside since February of 2003. Prior to joining the faculty at UC Riverside, Dr. Gerry was a Senior Public Health Biologist for the California Department of Public Health where he was responsible for protecting Californians from pathogens transmitted by insects and other vectors. Dr. Gerry additionally served as a medical entomologist in the United States Army Reserve, with over 26 years of active and reserve service prior to his retirement from the military in 2015. Research and extension efforts in his laboratory at UCR are focused on the biology, ecology, and integrated management of pest arthropods and disease vectors associated with animals. Recent research has included studying the dispersal and control of filth flies, investigating the role of biting flies in the transmission of disease agents, and evaluating integrated pest management techniques for control of nuisance and disease-transmitting insects.

267

Louis Armentano, Ph.D., graduated from Cornell University in 1975, earning a B. S. with distinction in Animal Science. He went to North Carolina State to study the use of by-products as feeds for dairy cattle and received an M.S. in Animal Nutrition. His Ph.D. was from Iowa State where the relationship between rumen carbohydrate fermentation and the metabolic processes of cattle was examined at the whole animal level. After a brief research appointment at Virginia Tech working with protein nutrition of dairy cows, Lou joined the Department of Dairy Science at Madison in 1983 as an Assistant Professor with teaching and research responsibilities. In addition to a program studying basic liver metabolism in cattle, Lou has maintained a program addressing use of by- product feedstuffs and their role in providing energy, fiber, and protein to dairy cows. His most recent research efforts have been to explain the effects of dietary fat on milk fat secretion, and resulted from examining the effects of the sometimes high levels of corn oil found in distillers grains. In addition to serving as a professor for 33 years at the University of Wisconsin-Madison, Lou had the pleasure of chairing the department for 8 busy and exciting years. Lou recently moved his appointment from Full Professor to Professor Emeritus, but is still actively involved in dairy research and outreach. He currently serves on the National Research Council dairy nutrition guidelines writing committee and is president of the American Dairy Science Association.

268 California Animal Nutrition Conference 2017 Steering Committee

Chairperson: Phillip Jardon, DVM, MPVM, is a Technical Consultant for Elanco Animal Health. He earned his DVM from Iowa State University and MPVM from UC Davis. Dr. Jardon has worked in the dairy industry for 26 years. He has a wide range of experience in research, dairy veterinary practice, nutritional consulting, and in technical service.

Vice Chairperson: Jason Brixey, M.S., P.A.S. - Consulting Animal Nutritionist Jason D. Brixey was born and raised in Crescent City, CA. He graduated in 2001 from Cal Poly San Luis Obispo with a Dairy Science and Ag Business Undergraduate Degree. He attended the University of Idaho in Moscow under Dr. Mark McGuire, graduated with a Masters of Science in 2003. He started his animal nutrition career with West Milling LLC in Phoenix, AZ as the Nutrition Technical Representative. In April of 2005, Jason was hired by Pine Creek Nutrition Service Inc. out of Denair, CA to work on dairy farms as a Consulting Animal Nutritionist. He became partner at Pine Creek Nutrition Service in January of 2008. Married to my lovely wife Jodie that I share in the duties of raising three wonderful children; Jack (7), Kaydee (4 ½), and Will (2).

Ex Officio: Ben Tarr, Adisseo USA Inc. Ben Tarr is a native Californian who grew up on his family’s cattle ranch in the foothills of the Sierra Nevada near Oakhurst, California. He holds a Bachelors of Science in Animal Science and a Masters of Agriculture in Animal Science Business from Texas A&M University. His career experiences have spanned from working in the commodity sector for Cargill Investment Services and Integrated Grain and Milling to livestock production with Cactus Feeders in Amarillo, Texas and Harris Ranch in Selma, California. Ben worked for Adisseo as their U.S. Ruminant Sales Manager prior to taking a position with Global Animal Products in Texas.

Committee Members:

Anthony Allen, Biomin USA Inc., Ruminant Key Account Manager for Western U.S. with Biomin since since May of 2010. He has worked for Nutrius, Foster Farms Commodities Division, and PM Ag Products on the dairy direct side before joining Biomin on the supplier side. Anthony received his B.S., Animal Science degree from Fresno State in 1995 and he is a Certified Dale Carnegie Trainer and teaches a couple of classes each year. He is a lifelong Fresno resident. Anthony and his wife Stephanie have three boys, Nathan, Garrett, and Sam.

269

Marc Etchebarne, Michel A. Etchebarne, Ph.D. Inc., Independent Dairy Nutritionist since 2010. USMC 2003-2008, Sergeant. Colorado State University graduate emphasis on Sheep, Feedlot, and Dairy Systems in 2010. ARPAS member, PAS.

Jennifer Heguy, is a native of California’s San Joaquin Valley. She received her B.S. in Animal Science, with an emphasis in Livestock and Dairy, at the University of Caifornia, Davis. In 2006, she received her M.S. degree at UC Davis, focusing on dairy cattle nutrition. Jennifer currently serves as the University of California Dairy Farm Advisor in Merced, Stanislaus and San Joaquin Counties where milk is a major agricultural commodity. Jennifer’s major program focus is improving silage and feeding management practices on California dairies.

David Ledgerwood graduated in 2004 with a BS degree in Animal Science focusing on livestock and dairy cattle from the University of California Davis. Upon graduating he worked in the university ruminant nutrition lab with Dr. Ed DePeters as a lab technician performing feed nutrient and milk analysis while assisting graduate students run various ruminant nutrition focused research trials. In 2007 he graduated with a MS degree in Animal Biology focusing on ruminant nutrition working with Dr. DePeters. After graduation in 2007 he worked as a lab and research program manager in the field of animal behavior/welfare on the UC Davis campus performing various research trials focused on improving cow comfort. In January of 2014 he accepted a job with the Veterinary Medicine Teaching and Research Center in Tulare as a research program manager for the clinical department performing various research projects covering cow behavior, calf health, and nutrition. In April of 2011 he was offered a position with Western Milling LLC working as a dairy nutritionist and currently works one on one with dairymen to find the nutrition program that works for their facility and cows. He also takes part in the quality control program at the Goshen mill and works with Quality Assurance to write and edit programs to ensure they produce quality products.

Heidi Rossow, Ph.D., is an Assistant Professor of Ruminant Nutrition with UC Davis Veterinary School located at the Veterinary Medicine Teaching and Research Center (VMTRC) in Tulare, California. Her areas of research are computer modeling of nutrient metabolism and systems analysis of dairy and beef production. She has authored over 20 publications and created several computer programs that evaluate rations and production for dairy and beef cattle, estimate body weight changes based on dietary information for humans, predict liver and adipose nutrient metabolism and track nitrogen and phosphorus balance for a dairy farm. Dr. Rossow is currently developing a nutrition program in conjunction with several farms in the central valley to teach veterinary students the role of nutrition in dairy management and disease.

270

Honorary Member:

Kyle Thompson, Ph.D. received his B.S. degree in animal science from Fresno State (2006) and his master's and Ph.D. degrees in animal science from Oklahoma State (2011/2015). He joined the Fresno State staff in the fall of 2016 after taking classes and teaching at Oklahoma State from January 2007-June 2016 and serving as the graduate student assistant manager of the campus dairy cattle center. His research included dairy nutrition research trials and lactating cow probiotics. He also assisted in research for bovine respiratory disease, rumen temperature bolus, milk production by weigh-suckle-weigh and swine antimicrobial replacements. He also assisted in 4-H and FFA Field Day dairy judging competitions. While in Stillwater, OK, he owned and operated Wild Acre Farms and Exotics, which raised ewes, game birds, free range hens and other fowl/animals, and produced grasses and winter wheat for grazing and hay production. As a Fresno State student, he worked in the sheep unit three years, served as a campus farm tour guide, and dairy unit herdsman and feed/hospital technician. He also worked as an exotic animal nutrition intern (2009) and global nutrition fellow at the San Diego Zoo (2013).

271 CALIFORNIAANIMAL NUTRITION CONFERENCE HISTORY

YEAR CHAIRPERSON COMPANY AFFILIATION 2016 Dr. Phillip Jardon, DVM, MPVM Elanco Animal Health 2015 Mr. Ben Tarr Adisseo USA Inc. 2014 Dr. Jeffrey M. DeFrain Zinpro Performance Minerals 2013 Mr. Doug DeGroff Diversified Dairy Solutions, LLC 2012 Mr. Eduardo Galo Novus International, Inc. 2011 Dr. Michael A. DeGroot DeGroot Dairy Consulting 2010 Dr. Jim Tully Pine Creek Nutrition Service, Inc. 2009 Mr. Michael Braun Phibro Animal Health 2008 Dr. Luis Rodriguez Zinpro Corporation 2007 Dr. Marit Arana A.L. Gilbert Company 2006 Mr. Dennis Ervin PAS Prince Agri Products, Inc. 2005 Dr. Lawson Spicer Nutri Management Inc. 2004 Dr. Luis Solorzano Purina Mills, Inc. 2003 Dr. Alfonso Mireles, Jr. Foster Farms 2002 Mr. Edmund Vieira Pine Creek Nutrition Service, Inc. 2001 Dr. Melinda Burrill California State Polytechnic University - Pomona 2000 Mr. Dave Fischer Foster Farms 1999 Dr. M. Steven Daugherty California State Polytechnic University - SLO 1998 Dr. Doug Dildey Alltech, Inc. 1997 Ms. Carla Price Nutritionist 1996 Dr. H.John Kuhl, Jr. Nest Egg Nutrition 1995 Mr. Dennis Ralston M. Rinus Boer Co., Inc. 1994 Dr. Doug Dildey Alltech, Inc. 1993 Dr. Mark Aseltine Consulting Animal Nutritionist 1992 Dr. Carl Old MacGowan-Smith Ltd. 1991 Mr. Nick Ohanesian Ohanesian & Associates 1990 Mr. Rod Johnson M. Rinus Boer Co., Inc. 1989 Mr. Timothy Riordan Nutri-Systems, Inc. 1988 Dr. Russ W. Van Hellen Great West Analytical 1987 Dr. John E. Trei California State Polytechnic University, Pomona 1986 Dr. A.A. Jimenez Ancon, Inc. 1985 Dr. Wm. A. Dudley-Cash Foster Farms 1984 Dr. Joel Kemper Penny-Newman Co. 1983 Dr. Alex J. Kutches O.H. Kruse Grain & Milling Co. 1982 Dr. Howard Waterhouse Bell Grain & Milling 1981 Mr. Don Ulrich Diamond Shamrock Chemical Co. 1980 Mr. Tom Geary PMS-West, Inc. 1979 Dr. Frank Parks Kemlin Industries 1978 Mr. Fred Pfaff Zacky Farms 1977 Mr. Rene Lastreto Diamond Shamrock Chemical Co. 1976 Mr. Rene Lastreto Diamond Shamrock Chemical Co. 1975 Dr. R.D. Hendershott Nulaid Foods 1974 Dr. R.D. Hendershott Nulaid Foods 1973 Dr. Leland Larsen Nutri-Systems, Inc. 1972 Dr. Leland Larsen Nutri-Systems, Inc. 1971 Mr. Rene Lastreto Diamond Shamrock Chemical Co. 272 CALIFORNIAANIMAL NUTRITION CONFERENCE HISTORY- Continued

YEAR CHAIRPERSON COMPANY AFFILIATION 1970 Mr. Fred Pfaff Balfour Guthrie 1969 Mr. Fred Pfaff Balfour Guthrie 1968 Mr. Fred Pfaff Balfour Guthrie 1967* Mr. Gary L. Frame J.G. Boswell Co. 1966* Mr. Gary L. Frame J.G. Boswell Co. 1965* Mr. Arne Jalonen Topper Feed Mills 1964* Mr. Arne Jalonen Topper Feed Mills 1963* Dr. W.P. Lehrer Albers Milling Co. 1962* Dr. H.J. Almquist The Grange Co. 1961* Dr. H.S. Wilgus The Ray Ewing Co. 1960* Mr. Bert Maxwell Nulaid Foods 1959* Mr. Bert Maxwell Nulaid Foods 1958* Mr. Robert Caldwell Anderson Smith Milling Co. 1957* Mr. Emery Johnson P.C.A., Los Angeles 1956* Mr. Emery Johnson P.C.A., Los Angeles 1955* Dr. H.J. Almquist The Grange Co. 1954* Dr. H.J. Almquist The Grange Co. 1953* Mr. Clifford Capps California Milling Co. 1951* Mr. Dolph Hill Golden Eagle Milling Co. 1950* Dr. H.J. Almquist The Grange Co. 1949* Dr. H.J. Almquist The Grange Co. 1948* Dr. H.J. Almquist The Grange Co.

* California Animal Industry Conference

273 History of the California Animal Nutrition Conference

The California Animal Nutrition Conference (CANC) originated in the 1940's as the California Animal Industry Conference, sponsored by the California Grain & Feed Association (CGFA). CGFA wanted to expand the continuing education program into a forum encompassing animal health, nutrition and management. The expectations were that communications between (nutritionists) industry, educational institutions and regulatory agencies would be improved. In 1972, CGFA discontinued sponsoring the Animal Industry Conference.

After the conference was discontinued, a small group of nutritionists began meeting annually in Fresno. Two or three invited speakers from industry or the universities presented information on nutrition, especially poultry.

In 1975 a set of organizational bylaws were developed by the steering committee. CANC was established and was provided support by CGFA. The CGFA Board of Directors appointed a chairperson annually and approved the steering committee. In 1978, Dr. Frank Parks, the Chairperson, requested that CANC be granted independent status and be established as a self-governing committee of CGFA. This request was granted.

For a few years, meetings were held in Fresno and Corona, California. For a couple of years starting in 1978, CANC published “Nutri-Facts”, a “newsletter” consisting of articles in animal production.

In 1979, donations were requested from industry companies to help keep registration fees low. During the 1980's and through the 1990's the attendance at CANC continued to grow as the quality of the conference improved and the conference became known nationwide. In the 1990's a pre-symposium was added. The pre-symposium is sponsored by a company selected by the CANC Steering Committee. This process allows the selected company to showcase its research and products. In the year 2000, posters on research by students were included.

Attendance at the conference has grown from 50 in the 1970's to over 300 attendees. To encourage attendance, different activities have been tried such as keynote speakers, skiing expeditions and a very successful barbeque dinner put on by the Animal Science Department at Fresno State University.

The California Grain & Feed Association has supported and allowed CANC to work and grow. The premise of the CGFA and CANC relationship is to work together to educate the feed industry with information for problem solving and to disseminate valuable research information. CANC is not an industry, university, or government entity, but a committee collectively working together for the good of agriculture in California.

274

STUDENT ABSTRACTS FOR

POSTER PRESENTATION

AT THE

CALIFORNIA ANIMAL

NUTRITION CONFERENCE

MAY 10 & 11, 2017

275

Estimation of the requirement for water and ecosystem benefits of cow-calf production on California rangelands1

E. Andreini*, J. Finzel†, D. Rao†, S. Larson†, and J.W. Oltjen*, †

*Department of Animal Science, University of California, Davis †University of California Cooperative Extension

ABSTRACT

Among other agricultural sectors, beef production is accused of using large amounts of water, and in an effort to reduce water use, some studies recommend decreasing or halting meat consumption. Beef production water footprints vary and some do not consider the tradeoffs associated with ecosystem benefits provided by cattle on rangeland. A static model depicting water use for cow-calf production on California rangeland was developed on an Excel spreadsheet. Range water use for beef production was modeled at two UC Agriculture and Natural Resources (ANR) Research and Extension Centers: Hopland (HREC) and Sierra Foothill (SFREC), and at the USDA Forest Service San Joaquin Experimental Range (SJREC). These three locations were chosen based on evapotranspiration (ET) zones and differences in forage production and rainfall. The model accounted for green water (i.e., water used for range forage production), and blue water (i.e., drinking water, and water used to grow alfalfa and irrigated pasture). As liters per kg of live weight, green water consumption was estimated to be 42,492 for HREC, 28,106 for SFREC, and 22,102 for SJREC. Blue water consumption as liters per kg of live weight was estimated to be 4,631 for HREC, 12,784 for SFREC, and 9,140 for SJREC. The model was sensitive to changes in range forage production and irrigated pasture use. Green water usage appears large; however, cattle consume less than 18% of the total water range forage plants use to grow. Given that green water is sourced from rainfall and is not designated for another use, it is misleading to associate negative environmental impacts to rangeland beef production based on these numbers. It is important to consider the water use associated with beef production in the context of ecosystem services cattle provide to rangelands, such as preventing grasslands from being converted to shrub lands, woodlands, or even forests, and the role grazing cattle play in managing and improving rangeland.

KEY WORDS: Water use, beef cattle, rangeland, ecosystem benefits

1 Acknowledgements: The authors would like to acknowledge and thank the Renewable Resource and Extension Act (RREA) grant for the funding of this project.

276 Description and evaluation of the AusBeef model of beef production

H.C. Dougherty 1, E. Kebreab1 , M. Evered2, B.A. Little3, A.B. Ingham3, R.S. Hegarty4, D. Pacheco5, & M.J. McPhee2

1Department of Animal Science, University of California, Davis, CA, 95616, 2 NSW DPI, Beef Industry Centre of Excellence, Armidale, Australia, 3 CSIRO Agriculture, St. Lucia, Australia, 4School of Env. Rural Science, University of New England, Armidale, Australia, 5 AgResearch Grasslands, Palmerston North, New Zealand.

As demand for animal products such as meat and milk increases, and concern over environmental impact grows, mechanistic models can be useful tools to better represent and understand ruminant systems and evaluate mitigation options to reduce greenhouse gas emissions without compromising productivity. AusBeef is a whole-animal, dynamic, mechanistic model of beef production that calculates methane emissions from net ruminal hydrogen balance. AusBeef incorporates a unique fermentation stoichiometry that represents four different microbial groups, as well as the effects of ruminal pH on microbial degradation of feed. The objectives of this study were to evaluate the performance of the AusBeef model of beef production with regard to predicting daily methane production (DMP, g/d), dry matter intake (DMI, kg/d), gross energy intake (GEI, MJ/d) and methane yield (MY, %GEI), using independent data derived from the literature. AusBeef predictions were compared for the full dataset (n=37) as well as for high-forage diets (n=21) and mixed diets (n=16) using a root mean square predicted error expressed as a percentage of the observed mean (RMSPE%). AusBeef predicted DMP with RMSPE% of 26.6, 30.1, and 21.3% for the full dataset, high-forage, and mixed diets, respectively. AusBeef predicted MY, DMI, and GEI with a RMSPE% of 38.5, 8.91, and 9.86% for the full dataset, respectively. There were prediction differences between forage and mixed diets with a RMSPE% of 9.32 and 8.43% for DMI; 6.38 and 11.1% for GEI and 41.7 and 28.4% for MY. AusBeef prediction errors for DMI ranged from -18 to +42%, with AusBeef underpredicting DMI 76% of the time. AusBeef underpredicted methane emissions 65% of the time, with prediction error ranging from - 51 to +59%, and underpredicted GEI 90% of the time, with prediction error ranging from -1 to +30%. Further studies are required to improve the prediction of methane on forage only diets.

Keywords: Modeling, beef cattle, sustainable agriculture, methane, greenhouse gases

277 Predicting in sacco rumen undegraded crude protein and rumen degraded neutral detergent fiber in canola meal

H.G. Gauthier, N.S. Swanepoel, P.H. Robinson Department of Animal Science UC Davis, Davis, CA

In sacco values for rumen undegraded crude protein (RUP) and rumen degraded neutral detergent fiber (dNDF30) are often estimated by incubating samples in the rumen of cows for 16 or 30 hours respectively. This utility of the method in real time, however, is limiting due to the time and expense involved in processing samples. Therefore, the ability to predict in sacco RUP or dNDF30 values based on more easily measured analytes would be useful to quickly estimate dNDF30 or RUP of a feed sample, as well as identify potential outliers. Our objective was to determine the predictive power of dry matter (DM), crude protein (CP), soluble crude protein (SolCP), acid detergent fiber

(ADF), neutral detergent fiber (aNDF), starch, lignin and ether extract (EE) on measured

RUP and dNDF30 values of canola meal (Table). Samples of canola meal (n=24) were collected at 2 week intervals from 9 commercial California dairy farms in the San

Joaquin Valley, and analyzed for nutrient composition. Linear models were fit to the data using R (2016), with DM, CP, SolCP, ADF, starch, lignin and EE as fixed effects.

Of the variables investigated, variations in RUP can be best (albeit not too well)

2 predicted by DM and SolCP (each r =0.37; Figure), and variation in dNDF30 is reasonably well predicted by aNDF (r2=0.67; Figure). Use of these more readily determined nutrient components as predictors of rumen degradability of CP and NDF

278 offer a potential alternative to the in sacco digestion procedure to provide a rapid estimate of the RUP and dNDF30 in canola meal.

Figure. Relationships of DM and SolCP with RUP and of aNDF with dNDF30.

Table. Average values and standard deviations of several analytes of the canola meal samples.

Analyte Average Standard Deviation

DM (%) 89.2 0.72

CP (% DM) 44.2 0.70

SolCP (% CP) 25.0 4.78

RUP (% CP) 25.5 3.95

aNDF (% DM) 30.0 3.67

dNDF30 (% NDF) 55.0 4.36

ADF (% DM) 23.9 1.81

Lignin(sa) (% DM) 9.1 1.16

Starch (% DM) 0.9 0.21

EE (% DM) 3.3 0.72

NEL (Mcal/kg DM) 1.67 0.04

279 Impact of monobutyrin supplementation in liquid diet on growth, health and intestinal development of preweaning calves L. K. Hilligsøe1, 2, J. E. Mendez2, A. M. Ehrlich2, R. Sygall3, H. Raybould2, P. Ji2 1University of Copenhagen, Denmark, 2University of California Davis, 3Perstop Feed & Food, Malmö Sweden

Butyric acid, a fermentation product in forestomach of ruminant, is naturally present in cow milk. Dietary supplementation of butyrate has a broad beneficial effect on growth and digestibility in weanling piglets. However, its effect in preweaning dairy calves is controversial. In this study, we hypothesized that supplementing butyric acid in form of its glycerol ester in milk may enhance its intestinal delivery and stimulate epithelial development in preweaning calves. Twenty-two Holstein bull calves (< 4 d old) were stratified by arriving BW and serum total protein and randomly assigned to treatments. Calves were fed milk replacer that supplemented with 0 (CON), 0.4 (LOW) or 0.8% (HIGH) of monobutyrin (MB, solid basis of milk replacer) until 8 wk of age. Milk replacer containing 27.6% CP and 14.4% crude fat (DM basis) were fed twice daily at 1.5% (solid basis) of BW which was updated weekly. Starter grain and water were provided for ad libitum consumption during the study. Scores for health (respiration, diarrhea, and alertness) were assigned once daily. The appearance and respiration were based on a scale from 1 to 5, where 1 was alert, bright and clear eyes, ears up and/or normal breathing and 5 was flat on side with severe depression and/or chronic dry cough weak or rapid breathing. Fecal and nasal score was given on a 1-4 scale where 1 was normal moist nose and/or firm and well-formed but not hard faeces, and 5 was copious, bilateral mucopurulent discharge and/or liquid faeces. BW was measured at arrival and weekly, and body frame parameters were measured at arrival and on wk 4, 6 and 8. Calves were weaned and euthanized on wk 8. Tissue, mucosa and digesta samples from GIT were collected. The jejunum epithelial permeability was measured through ussing chamber immediately after collection. The data was subject to analysis of variance using mixed procedure of SAS. The categorical data of health status was analyzed using Chi-square test. Our results showed that body weight was not significantly different among treatment groups, whereas wither height and body length were significantly greater (P < 0.05) in calves from the LOW group than those from the CON group on wk 6 and 8. Supplementation of MB tended to increase (P = 0.08) hip height. Calves from the LOW group had the highest intake of starter grain from wk 4 to 6 (P < 0.01) followed by calves from the CON and HIGH groups. The risk of diarrhea (fecal score > 2; 4-scale score, 1=firm, 4=watery) was not affected by MB supplementation. However, calves of LOW groups had the lowest risk of respiratory distress (respiratory score > 2; 5-scale score, 1=normal, 5=chronic dry cough and rapid breathing) than that of the other two groups (P=0.024). Despite lack of significance (P = 0.17), villus height of jejunum epithelium was the highest in LOW followed by HIGH and CON. The crypt depth was not affected by treatment. However, the ratio of villus height to crypt depth was significantly higher (P = 0.04) in LOW than that in CON. MB increased (P < 0.05) mRNA of tight junction proteins (CLDN1 and OCLN) in jejunal mucosa. Para- and transcellular permeability were not affected by treatment. In conclusion, low dose of MB supplementation in milk replacer moderately improved growth performance and jejunal epithelial development in preweaning calves. Keywords: monobutyrin calf preweaning

280 Effect of phytogenic feed supplements added to starter grain on weight gain and rumen development in Holstein calves

HA Rossow, KE Mitchell, A Johnson, B Miller (Biomin America Inc.)

The goal of pre wean calf operations is to maximize rumen development and weight gain. Feed supplements that increase starter intake should also encourage rumen development and increase weight gain. The objective of this study was to compare rumen development and body weight gain in pre wean calves given two different starter supplements, phytogenic blend A (A) or phytogenic blend B (B) (Biomin, San Antonio, TX) or no supplement (Control) at a commercial calf ranch. One hundred and twenty four holstein calves were randomly assigned to 1 of three treatments, Control, A or B, at 1 d of age. Control (nothing added), A or B were added to individual feed buckets at each feeding at the rate of 0.25g / kg starter at AM and PM feedings. Both starter intake and milk intakes were assessed daily. Calves were weighed at enrollment and at weaning, and blood samples were collected from a subset of 38 calves and analyzed for glucose (Glu, mg/dl) and β-hydroxybutyrate levels (BHBA, mmol/L) with Precision Extra blood meters (Abbott Diabetes Care, Inc., Alameda, CA) to assess rumen development. Weekly average DMI, milk intake, Glu and BHBA were analyzed using the Mixed procedure of SAS (v. 9.4) with repeated measures by calf, fixed effects treatments and random effect week. Weekly average DMI (P <0.01), BHBA (P < 0.01) and Glu (P < 0.01) were different by week but not by treatment. However, weekly milk intake was less for group D (P < 0.05). Total DMI, initial bodyweight, final body weight and gain were analyzed using the Mixed procedure of SAS with repeated measures by calf, fixed effects hutch, gender, birthdate. There were no differences in initial bodyweight and effects of hutch, birthdate or gender among treatments. Product B group was numerically greater in total DMI, gain, ADG and had faster rumen development indicated by overall higher BHBA values but differences were not significant among treatments. Therefore Supplementation with B decreased milk intake but calves maintained similar starter DMI, gain and ADG.

281 Effects of dietary β-glucan on systemic immunity of weanling pigs experimentally infected with a

pathogenic E. coli

K. Kim,1 V. Perng,1 J. Chase,1 X. Li,1, E.R. Atwill,1 R. Whelan,2 A. Sokale,3 Y. Liu1

1University of California, Davis, CA, 2Evonik Nutrition & Care GmbH, Germany, 3Evonik Corporation,

Kennesaw, GA

ProGlucanTM is 100% dried algae, Euglena gracillus, which contains approximately 54% β-1,3-glucan. The objective of this experiment was to investigate the influence of dietary supplementation of ProGlucan on systemic immunity of weaned pigs experimentally infected with a pathogenic F-18 E. coli. Weaned pigs (n

= 36, 7.69 ± 0.77 kg BW) were individually housed in disease containment rooms and randomly allotted to one of three dietary treatments with 12 replicate pigs per treatment. The three diets were a nursery basal diet (control), and 2 additional diets containing with either 100 or 200 mg/kg of ProGlucan added to the basal diet. The experiment lasted 17 d [5 d before and 12 d after the first inoculation (d 0)]. The inoculum used in this experiment was F-18 E. coli, containing LT, STb, and SLT-2 toxins. The inoculation doses were 1010 cfu/3 mL oral dose daily for 3 days. Blood samples were collected right before E. coli challenge, and on d 2, 5, 8, and 12 post-inoculation (PI). Total and differential blood cell count were analyzed by CBC test. The concentration of CD4+ T cells, CD8+ T cells, and B cells were analyzed by flow cytometry. The concentrations of cytokines (TNF-α, IL-6, and IL-10), cortisol, and haptoglobin in serum samples were analyzed by enzyme-linked immunosorbent assay. All data were analyzed by ANOVA using the PROC

MIXED of SAS in a randomized complete block design and repeat measurement by time. Pigs fed with 100 mg/kg of ProGlucan had less (P < 0.05) total white blood cells and neutrophils on d 8 PI, had greater (P <

0.05) CD4+ T cells on d 2 PI, and had less (P < 0.05) serum haptoglobin and cortisol on d 5 and 12 PI, compared with pigs fed the control diet. Supplementation of 200 mg/kg of ProGlucan increased (P < 0.05) the percentage of CD8+ T cells on d 5 PI, but reduced (P < 0.05) the percentage of CD8+ T cells on d 12

PI, compared with the control diet. Inclusion of 200 mg/kg of ProGlucan also decreased (P < 0.05) serum haptoglobin on d 2 and 5 PI, reduced (P < 0.05) TNF-α concentration on d 5 PI, and reduced (P < 0.05) serum cortisol on d 5, 8, and 12 PI. Results indicate that supplementation of ProGlucan may regulate systemic immunity and reduce systemic inflammation of weaned pigs caused by E. coli infection.

Key words: ProGlucan, systemic immunity, weaned pigs

282 Colostrum mineral concentrations and their association with calcemic status at calving in

Jersey cows

J Chiozza-Logroño*1, A Valldecabres1, A Lago2, N Silva-del-Río1; Veterinary Medicine

Teaching and Research Center, University of California Davis, Tulare, CA, USA1, DairyExperts

Inc., Tulare, CA, USA2

The aim of the present study was to evaluate the association of postpartum calcemic status and colostrum concentration of Ca, P, Mg, K, Na, Fe, Zn and Cu on 131 multiparous Jersey cows.

Colostrum samples were harvested at 9 h 36 min (± 3 h 36 min) after calving and analyzed for mineral concentration by Inductively Coupled Plasma – Optical Emission Spectrometry. Final colostrum weigh was recorded at milking. Blood samples for serum Ca analyses were collected from the coccygeal vessels within 6 h after calving. Based on serum Ca concentration, cows were classified as hypocalcemic (SHC; Ca ≤ 8.5 mg/dL; n = 103) and normocalcemic (NC; Ca >

8.5 mg/dL; n = 28). Despcriptive statistics, including first (Q1), second (Q2) and third (Q3) quartiles of colostrum mineral concentrations based on calcemic status at calving are shown in the Table 1. Associations among calcemic status were analyzed using mixed models with

MIXED procedures of SAS. There was a tendency (P = 0.07) for higher colostrum weigh on

SCH cows (4.2 kg) than NC cows (3.2 kg).

Cows with SHC had higher colostrum P concentration (1400.13 vs. 1140.43 mg/kg; P < 0.01)

Mg (338.88 vs. 299.52. mg/kg; P < 0.05), K (1494.87 vs.1302.73 mg/kg; P < 0.01) and Zn

(18.54 vs.15.25 mg/kg; P < 0.05) than NC cows, but lower Na (822.19 vs. 1003.73 mg/kg; P <

0.05).

Cows with SHC had higher colostrum excretion P (P < 0.05) and Mg (P < 0.05) than NC cows.

283 Our results show that calcemic status tends to affect colostrum yield and is associated with mineral concentration at calving.

Table 1. Colostrum mineral concentrations at first milking

Ca P Mg K Na Fe Zn(mg/kg) Cu(mg/kg) SHC (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg)

Q1 2000 1100 280 1287 619 0.51 13 0.16

Q2 2200 1400 330 1428 759 0.64 18 0.21

Q3 2600 1600 380 1677 980 0.79 23 0.26

Q1 1600 730 230 936 763 0.66 8.4 0.18

Q2 2100 1100 280 1248 913 0.73 12 0.21

Q3 2500 1500 340 1638 1276 0.87 22 0.30

KEYWORDS: colostrum minerals, hypocalcemia, Jersey cow

284 Monitoring ketosis in a commercial Holstein and Jersey herd Kelly Mitchell University of California, Davis

Subclinical ketosis, a common metabolic issue in the transition period, is estimated to cost $78 per case due to decreased milk production, reduced fertility, displaced abomasum, and other health issues (Geishauser et al., 2001). Considering the potential loss of profit to a producer, this study focused on identifying the most effective monitoring program to identify at risk cows. Holstein (n=54) and Jersey (n=52) multiparous cows at a commercial dairy were enrolled during the prepartum period and then followed to approximately 21 DIM. Weekly blood samples were analyzed for Glucose (Glu mg/dl) and β-hydroxybutyric acid (BHBA, mmol/L) using Nova Max® (Nova Diabetes Care, Inc., Billerica, MA). Weekly milk tests were taken in first 21 DIM and compared to BHBA and Glu values recorded that same week. Both Jerseys and Holsteins both experience a decrease in Glu and an increase in BHBAs from prepartum to postpartum (P<0.0001). However, Jerseys were lower than Holsteins for Glu and BHBA levels during early lactation with values of 2.45 mg/dl and 0.13 mmol/L, respectively (P=0.061, P<0.0001). Due to this difference, the rest of analysis were run with breeds separated. Blood categories were assigned as 1 (less than) or 2 (greater than) for multiple thresholds for both BHBA and Glu levels. Initially, blood parameters were compared to health issue risk and milk production level individually, but neither alone yielded any significant results. Due to the lower incidence of hyperketonemia, Jerseys did not have enough samples available for analysis; only 5 cows had >1.0 mmol/L BHBA versus 25 Holstein cows. For Holsteins, using both Glu and BHBA blood levels as markers were more accurate for identifying suppressed milk production. They had increased risk of health issues and a decrease of 5.44 kg/d of milk when BHBA > 1.0 mmol/L and Glu<50 mg/dl (P=0.099). The highest incidence of ketosis for both breeds was in the first week of production. Therefore a monitoring program within the first week of lactation would be more beneficial for Holsteins. Based on these results, both BHBA and Glu testing is recommended. Since Glu strips are less expensive, Glu can be used to prescreen for hypoglycemia first, and then BHBA can be used to confirm the diagnosis if Glu values are low.

285 286 '# ! '#$$ ( # " " # # $ $! !&#!#%% !#$#' &%# %$ #$ !! %$$% $ ##$% !# '"&% # #'$% %# & &%%*#. % * #$&$( #(, # $ % $%! #% %,$!**#* ##$ # /$#% %#*34. !# $$ $$%%*%#%%!# &%* # $&#$ #$ #$ # % $.&# %$# %% , #! &#$ ! 3 ( 4 %!#%&#3&!4 %$$&#$# % &$$ % #$ $"&%*%#%$# %% $$%+.% #$ $&$! $%* # ! $%&## $#% %%$# %% !# $$.' % & *& $)$%#%'% !%$ % %#!$ %,% !) !# $$$ $ # %&*& #$% 2( % & )!%& $#$ &% $,$!*%*& #% '#!$%. $&# $%*,! , %!#%&#, #*%%#3 4 :=(1%*! # $!$%:; ##*#$ %(%% #%#+ $&#$3$$% ;9 !%4 #! ! .$!$(# % # !%? % $3$$; %%% !,; &%?% '%$ $,;5G(*&!$4 $ % &%1!. # ' % $$% ;!%$# % 3$$ &%#19% :9 - #1:9% ;9 4.

$ ! $% ) & #($(# (%(% $%* # #$ !$3:9% :A 0%<4,((#&#% % &%# #$3:B% <=0%<4. #$# % %% ! %!$# $%*% % $# %$$.$#$&%$(# $$% %(% (% % # # &%# #$ % ! $ #$. %# %$$%&*$%%$$!$# # #$ (# $$% %! !$3$$<.>;% =.;<4,( &%# #$# & #'#! 3$$ <.>;% >.994,$$ ( &#;. ##%

287 %( ! %!#%&#3&#<4$!#!$$&#!#$ $%$&$%$%%$ %!#%&#$! #!#% # $! .

#%#$%$ :; # $!$%# % % $ !%$. % !% $%*30%<4 ! !#%&#364 3H4

&%# :A.@3<.<<4 <.@939.:=4 @@.B3B.?;4 <:.;3<.:<4 % ! # 4 A>.>3@.>B4 <<.93;.>B4

&%# ::.A3;.?;4 <.B?39.;B4 @A.>3:9.B94 ;B.>3<.@?4 $ # ;<.>3<.?:4 <.@@39.:=4 A>.<3A.:<4 <;.<3:.B?4 '#34 (# $%* &%# #$!$*&% (# ($&$* $%&# $$%*& #%& #* '#.$ &%# #$# $#% % & #* '#$%*% % $ # $%&#&% $ ##% '#*$ ( ' % # '#%$&#& #%!$% '#$. #$ $%* % %% ! %!$, %$$, %$%%$ %$ (%*!!$ $ %!$%%*$$%%% !&% %(*!$#&%. ( $$% %! # #$, !#% &%#,$&$%$%! $$%*%%$&# $! #* &## $ !$!# #% ! ,!# *&% &%# # $ )! $% $ )* %( %& #*!$% $. &# (1&!$%&*$#%#+ %# %% !# 0*$% & %$ #%)! $ :9 # $!$&# &% ##% %%# & #$% !#!#$! ,($ # $!$.

# )! %$ ! # &%#391:9 4 $ %!#%&#0! #3:91;9 4 #$. #% $! #$.

&& -!!!!' ,$('%' &! *.!!"

%& %!

$! %! '!! )! )!"!! "!! "#!

288 289 Immunological and metabolic responses of lactating dairy cows fed diets supplemented with exogenous β-mannanase enzyme (CTCzyme)

B.M. Roque1, G.C Reyes1, J.A.D.R.N. Appuhamy1, T.A Tewoldebrhan1, J.J. Lee2, S. Seo3, and E. Kebreab1. Department of Animal Science, University of California, Davis, U.S.A.1, 1CTCBio Inc., Seoul, Republic of Korea2, Department of Animal Biosystem Sciences, Chungnam National University, Daejeon, Republic of Korea.3

Hemicellulose plays an important role in maintaining cell wall structure and accounts for a quarter of total plant biomass, thus making it a considerable anti-nutritive factor in livestock diets. Exogenous fibrolytic enzymes have been used to improve feed efficiency by releasing nutrients bound in complex feed matrices such as hemicellulose. β-mannanase is an exogenous fibrolytic enzyme that is known to hydrolyze mannan structures found in hemicellulose matrices. β-mannanase has been suggested to act in three main ways; 1) reduction of feed viscosity, 2) improvement of energy metabolism, and 3) decreased immune stimulation. The objective of this study was to determine the effects of β- mannanase supplementation on immunological and metabolic responses in lactating Holstein dairy cows. Two weeks after calving, twenty Holstein cows (milk yield = 43 ± 10 kg/d), blocked by parity, were assigned to one of two diets for approximately 182 days. All cows were housed in the same environment and fed the same basal diet. The basal diet of the treatment group was supplemented with β-mannanase (CTCzyme) at 0.1% of concentrate DM. Haptoglobin, Immunoglobulin G (IgG) and somatic cell counts (SCC) were analyzed as a proxy for immune responses and non-esterified fatty acids (NEFA) were analyzed to explore metabolic responses. Blood samples were taken weekly and were analyzed for immune and metabolic markers. Milk samples were collected twice daily and were analyzed separately for SCC. Cows fed β-mannanase showed tendencies for reduced Haptoglobin levels (P=0.06), regardless of parity. Specifically, there was as significant reduction in blood Haptoglobin levels (P=0.01) in supplemented multiparous cows, compared to control multiparous cows, indicating that β-mannanase was associated with decreased systemic inflammation. Furthermore, NEFA levels tended to be lower in cows fed β-mannanase (P=0.08), regardless of parity, suggesting that β-mannanase was associated with improved energy balance during early to mid-lactation. β-mannanase supplemented cows, regardless of parity, displayed slight indications of lowered SCC, numerically, however was not statistically different (P=0.18). There was no differences in IgG response were recorded between treatment and control cows, regardless of parity.

Keywords: β-mannanase, fibrolytic enzymes, immune response, lactating cows

290 Effect of prophylactic oral calcium supplementation on postpartum mineral and metabolic status on multiparous Jersey cows

A. Valldecabres,* J. A. A. Pires,† and N. Silva-del-Río*‡

*Veterinary Medicine Teaching and Research Center, 18830 Road 112, Tulare, CA 93274

† UMR1213 Herbivores, INRA, VetAgroSup, Saint-Genes-Champanelle, France

‡Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis 95616

ABSTRACT

The effects of prophylactic oral Ca supplementation on blood mineral and metabolic status, subclinical ketosis, and clinical endometritis prevalence were evaluated on 205 multiparous Jersey cows housed in a commercial dairy. After calving, cows were randomly assigned to receive no oral Ca supplementation (control; n = 105) or 2 doses of oral Ca each containing approximately 50 g of Ca (CaOS; n = 100; QuadricalMINI Ca boluses; Bio-Vet,

Barneveld, WI) at 0 and 1 days in milk (DIM). Blood samples for analyses of serum minerals

(Ca, P, Mg, K, Na, Fe, Zn, and Cu) were collected before and 1 h after treatment administration at

0 and 1 DIM and also at 2 DIM. A subset of 74 cows was evaluated for plasma glucose and fatty acids concentrations at 0 and 1 DIM before treatment administration and at 2 DIM. Urine pH was measured immediately before, and 1 h after the administration of each oral Ca dose. Blood β- hydroxybutyrate (BHB) concentration was evaluated at 5, 8, and 11 DIM. Clinical endometritis

291 was assessed once between 21 and 40 DIM. Cows were classified according to their initial calcemic status (Ca-status) as normocalcemic (NC; Ca >8.5 mg/dL) or subclinically hypocalcemic (SHC; Ca ≤8.5 mg/dL). After treatment, serum Ca concentration was higher for

CaOS than control cows (8.53 vs. 8.24 mg/dL). Initial calcemic status had a significant effect on treatment response; SHC showed a greater increase in Ca levels than NC after oral Ca dose administration. Oral Ca supplementation reduced the prevalence of SHC (Ca ≤8.5 mg/dL) 1 h after treatment administration at 0 DIM (32 vs. 71%) and at 1 DIM (41 vs. 64%) for CaOS and control cows, respectively. However, at 2 DIM the prevalence of SCH tended to be higher for

CaOS than control cows (70 vs. 44%). Serum Mg concentration was higher for control-SCH cows. Regardless of Ca-status, serum K concentration was higher in CaOS than control cows

(4.68 vs. 4.53 mEq/L). Plasma glucose concentration tended to be lower for CaOS than control cows (53.1 vs. 55.4 mg/dL). Higher plasma fatty acids concentration was observed for CaOS compared to control cows at 2 DIM (0.43 vs. 0.35 mEq/dL). Urine pH was lower for CaOS than control cows (6.10 vs. 7.04). No treatment effect was observed on subclinical ketosis or clinical endometritis prevalence. These results suggest that postpartum Ca levels can be increased with oral Ca supplementation, but the response to treatment varies with initial calcemic status.

Keywords: oral calcium supplementation, subclinical hypocalcemia, dairy cow.

292 Effects of an immunomodulatory feed additive on peripheral blood neutrophil function of transition Holstein cows

Z. H. Wu1,3, Y. Yu1,2, G. M. Alugongo1, J. X. Xiao1, J. H. Li1, Y. X. Li2, Y. J. Wang1, S. L. Li1, P. Ji, and Z. J. Cao1* 1 State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China 2 College of Animal Science and Technology, Henan University of Science and Technology, Luoyang, Henan, 471003, China 3 Department of Animal Science, University of California, Davis, Davis, CA, US

Transition cow typically experience immunosuppression with dysregulated neutrophil functions (e.g. compromised phagocytosis), which is causally associated with increased risk of infections. To enhance neutrophil immune competence has significant bearing with wellbeing of transition dairy cattle. In current study, we investigated the effect of OmniGen-AF® (OG; Phibro Animal Health, Quincy, IL, USA) in modulation of neutrophil function of transition cows. Forty-eight multiparous cows were stratified by parity, somatic cell count (SCC) and expected calving date and randomly assigned to 3 treatments that OG was fed at 0 g/head/day (CON), 60 g/head/day (OG60; recommended value), or 90 g/head/day (OG90, 1.5× recommended value). The OG was added from 60 d before parturition to 28 DIM, and removed from all treatment groups during 29-35 DIM. Blood samples were collected (at 0800 h) on d -60, -28, -14, -7, 1, 7, 14, 28, 32, 35 for analysis of PMN phagocytosis and gene expression. A mixed-effects model with repeated measures and generalized linear model that included the effect of treatment at each sampling point were used for the data analysis. The results showed that neutrophil phagocytosis of S. aureus and E. coli was enhanced and tended to be enhanced by OG (P < 0.05 and P = 0.086, respectively) from 28 d before parturition to 28 DIM. Cows in OG60 had higher neutrophil phagocytosis of S. aureus and E. coli compared with that of cows in CON group from 28 d before parturition to 28 DIM (P < 0.05). Neutrophil phagocytosis of S. aureus and E. coli was higher and tended to be higher for OG60 than that of CON on 35 DIM (P < 0.05 and P = 0.094, respectively). The relative gene expressions of IL-8 and L-selectin was up- regulated and tended to be up-regulated by OG (P < 0.05 and P = 0.095, respectively) from 60 d before parturition to 28 DIM such that cows in OG60 had higher L-selectin and IL-8 gene expression than that of CON (P < 0.05 and P < 0.01, respectively). L-selectin gene expression of OG60 was greater than that of OG90 (P < 0.05), and the relative expression of IL-8 gene tended to be higher for OG60 compared with that of CON (P = 0.067) on 35 DIM. In conclusion, feeding OG at 60 g/head/d (recommended value) from dry-off period was effective in maintaining peripheral blood neutrophil function in transition dairy cows, and it is not necessary to feed OG beyond the recommended value. Key words: OmniGen-AF, neutrophil phagocytosis, IL-8, L-selectin

293